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The meaning of public messages such as "One in x people gets cancer" or "One in y people gets cancer by age z" can be improved. One assumption commonly invoked is that there is no other cause of death, a confusing assumption. We develop a light bulb model to clarify cumulative risk and we use Markov chain modeling, incorporating the assumption widely in place, to evaluate transition probabilities. Age-progression in the cancer risk is then reported on Australian data. Future modelling can elicit realistic assumptions.

Cancer Risk Messages: A Light Bulb Model

2018-07-09 13:58:20

Ka C. Chan, Ruth F. G. Williams, Christopher T. Lenard, Terence M. Mills

http://arxiv.org/abs/1807.03040v2, http://arxiv.org/pdf/1807.03040v2

econ.EM

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Statements for public health purposes such as "1 in 2 will get cancer by age 85" have appeared in public spaces. The meaning drawn from such statements affects economic welfare, not just public health. Both markets and government use risk information on all kinds of risks, useful information can, in turn, improve economic welfare, however inaccuracy can lower it. We adapt the contingency table approach so that a quoted risk is cross-classified with the states of nature. We show that bureaucratic objective functions regarding the accuracy of a reported cancer risk can then be stated.

Cancer Risk Messages: Public Health and Economic Welfare

2018-07-09 14:18:01

Ruth F. G. Williams, Ka C. Chan, Christopher T. Lenard, Terence M. Mills

http://arxiv.org/abs/1807.03045v2, http://arxiv.org/pdf/1807.03045v2

econ.EM

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This paper applies economic concepts from measuring income inequality to an exercise in assessing spatial inequality in cancer service access in regional areas. We propose a mathematical model for accessing chemotherapy among local government areas (LGAs). Our model incorporates a distance factor. With a simulation we report results for a single inequality measure: the Lorenz curve is depicted for our illustrative data. We develop this approach in order to move incrementally towards its application to actual data and real-world health service regions. We seek to develop the exercises that can lead policy makers to relevant policy information on the most useful data collections to be collected and modeling for cancer service access in regional areas.

Simulation Modelling of Inequality in Cancer Service Access

2018-07-09 14:25:38

Ka C. Chan, Ruth F. G. Williams, Christopher T. Lenard, Terence M. Mills

http://dx.doi.org/10.1080/27707571.2022.2127188, http://arxiv.org/abs/1807.03048v1, http://arxiv.org/pdf/1807.03048v1

econ.EM

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The data mining technique of time series clustering is well established in many fields. However, as an unsupervised learning method, it requires making choices that are nontrivially influenced by the nature of the data involved. The aim of this paper is to verify usefulness of the time series clustering method for macroeconomics research, and to develop the most suitable methodology. By extensively testing various possibilities, we arrive at a choice of a dissimilarity measure (compression-based dissimilarity measure, or CDM) which is particularly suitable for clustering macroeconomic variables. We check that the results are stable in time and reflect large-scale phenomena such as crises. We also successfully apply our findings to analysis of national economies, specifically to identifying their structural relations.

Clustering Macroeconomic Time Series

2018-07-11 11:51:41

Iwo Augustyński, Paweł Laskoś-Grabowski

http://dx.doi.org/10.15611/eada.2018.2.06, http://arxiv.org/abs/1807.04004v2, http://arxiv.org/pdf/1807.04004v2

econ.EM

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This paper re-examines the problem of estimating risk premia in linear factor pricing models. Typically, the data used in the empirical literature are characterized by weakness of some pricing factors, strong cross-sectional dependence in the errors, and (moderately) high cross-sectional dimensionality. Using an asymptotic framework where the number of assets/portfolios grows with the time span of the data while the risk exposures of weak factors are local-to-zero, we show that the conventional two-pass estimation procedure delivers inconsistent estimates of the risk premia. We propose a new estimation procedure based on sample-splitting instrumental variables regression. The proposed estimator of risk premia is robust to weak included factors and to the presence of strong unaccounted cross-sectional error dependence. We derive the many-asset weak factor asymptotic distribution of the proposed estimator, show how to construct its standard errors, verify its performance in simulations, and revisit some empirical studies.

Factor models with many assets: strong factors, weak factors, and the two-pass procedure

2018-07-11 14:53:19

Stanislav Anatolyev, Anna Mikusheva

http://arxiv.org/abs/1807.04094v2, http://arxiv.org/pdf/1807.04094v2

econ.EM

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This paper analyzes the bank lending channel and the heterogeneous effects on the euro area, providing evidence that the channel is indeed working. The analysis of the transmission mechanism is based on structural impulse responses to an unconventional monetary policy shock on bank loans. The Bank Lending Survey (BLS) is exploited in order to get insights on developments of loan demand and supply. The contribution of this paper is to use country-specific data to analyze the consequences of unconventional monetary policy, instead of taking an aggregate stance by using euro area data. This approach provides a deeper understanding of the bank lending channel and its effects. That is, an expansionary monetary policy shock leads to an increase in loan demand, supply and output growth. A small north-south disparity between the countries can be observed.

Heterogeneous Effects of Unconventional Monetary Policy on Loan Demand and Supply. Insights from the Bank Lending Survey

2018-07-11 17:36:21

Martin Guth

http://arxiv.org/abs/1807.04161v1, http://arxiv.org/pdf/1807.04161v1

econ.EM

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I present a dynamic, voluntary contribution mechanism, public good game and derive its potential outcomes. In each period, players endogenously determine contribution productivity by engaging in costly investment. The level of contribution productivity carries from period to period, creating a dynamic link between periods. The investment mimics investing in the stock of technology for producing public goods such as national defense or a clean environment. After investing, players decide how much of their remaining money to contribute to provision of the public good, as in traditional public good games. I analyze three kinds of outcomes of the game: the lowest payoff outcome, the Nash Equilibria, and socially optimal behavior. In the lowest payoff outcome, all players receive payoffs of zero. Nash Equilibrium occurs when players invest any amount and contribute all or nothing depending on the contribution productivity. Therefore, there are infinitely many Nash Equilibria strategies. Finally, the socially optimal result occurs when players invest everything in early periods, then at some point switch to contributing everything. My goal is to discover and explain this point. I use mathematical analysis and computer simulation to derive the results.

Analysis of a Dynamic Voluntary Contribution Mechanism Public Good Game

2018-07-12 17:13:41

Dmytro Bogatov

http://arxiv.org/abs/1807.04621v2, http://arxiv.org/pdf/1807.04621v2

econ.EM

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Wang and Tchetgen Tchetgen (2017) studied identification and estimation of the average treatment effect when some confounders are unmeasured. Under their identification condition, they showed that the semiparametric efficient influence function depends on five unknown functionals. They proposed to parameterize all functionals and estimate the average treatment effect from the efficient influence function by replacing the unknown functionals with estimated functionals. They established that their estimator is consistent when certain functionals are correctly specified and attains the semiparametric efficiency bound when all functionals are correctly specified. In applications, it is likely that those functionals could all be misspecified. Consequently their estimator could be inconsistent or consistent but not efficient. This paper presents an alternative estimator that does not require parameterization of any of the functionals. We establish that the proposed estimator is always consistent and always attains the semiparametric efficiency bound. A simple and intuitive estimator of the asymptotic variance is presented, and a small scale simulation study reveals that the proposed estimation outperforms the existing alternatives in finite samples.

A Simple and Efficient Estimation of the Average Treatment Effect in the Presence of Unmeasured Confounders

2018-07-16 07:42:01

Chunrong Ai, Lukang Huang, Zheng Zhang

http://arxiv.org/abs/1807.05678v1, http://arxiv.org/pdf/1807.05678v1

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This paper analyzes how the legalization of same-sex marriage in the U.S. affected gay and lesbian couples in the labor market. Results from a difference-in-difference model show that both partners in same-sex couples were more likely to be employed, to have a full-time contract, and to work longer hours in states that legalized same-sex marriage. In line with a theoretical search model of discrimination, suggestive empirical evidence supports the hypothesis that marriage equality led to an improvement in employment outcomes among gays and lesbians and lower occupational segregation thanks to a decrease in discrimination towards sexual minorities.

Pink Work: Same-Sex Marriage, Employment and Discrimination

2018-07-18 01:57:39

Dario Sansone

http://arxiv.org/abs/1807.06698v1, http://arxiv.org/pdf/1807.06698v1

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Regression quantiles have asymptotic variances that depend on the conditional densities of the response variable given regressors. This paper develops a new estimate of the asymptotic variance of regression quantiles that leads any resulting Wald-type test or confidence region to behave as well in large samples as its infeasible counterpart in which the true conditional response densities are embedded. We give explicit guidance on implementing the new variance estimator to control adaptively the size of any resulting Wald-type test. Monte Carlo evidence indicates the potential of our approach to deliver powerful tests of heterogeneity of quantile treatment effects in covariates with good size performance over different quantile levels, data-generating processes and sample sizes. We also include an empirical example. Supplementary material is available online.

Quantile-Regression Inference With Adaptive Control of Size

2018-07-18 17:40:36

Juan Carlos Escanciano, Chuan Goh

http://dx.doi.org/10.1080/01621459.2018.1505624, http://arxiv.org/abs/1807.06977v2, http://arxiv.org/pdf/1807.06977v2

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The accumulation of knowledge required to produce economic value is a process that often relates to nations economic growth. Such a relationship, however, is misleading when the proxy of such accumulation is the average years of education. In this paper, we show that the predictive power of this proxy started to dwindle in 1990 when nations schooling began to homogenized. We propose a metric of human capital that is less sensitive than average years of education and remains as a significant predictor of economic growth when tested with both cross-section data and panel data. We argue that future research on economic growth will discard educational variables based on quantity as predictor given the thresholds that these variables are reaching.

A New Index of Human Capital to Predict Economic Growth

2018-07-18 20:34:27

Henry Laverde, Juan C. Correa, Klaus Jaffe

http://arxiv.org/abs/1807.07051v1, http://arxiv.org/pdf/1807.07051v1

econ.EM

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The public debt and deficit ceilings of the Maastricht Treaty are the subject of recurring controversy. First, there is debate about the role and impact of these criteria in the initial phase of the introduction of the single currency. Secondly, it must be specified how these will then be applied, in a permanent regime, when the single currency is well established.

Stability in EMU

2018-07-20 10:53:14

Theo Peeters

http://arxiv.org/abs/1807.07730v1, http://arxiv.org/pdf/1807.07730v1

econ.EM

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If multiway cluster-robust standard errors are used routinely in applied economics, surprisingly few theoretical results justify this practice. This paper aims to fill this gap. We first prove, under nearly the same conditions as with i.i.d. data, the weak convergence of empirical processes under multiway clustering. This result implies central limit theorems for sample averages but is also key for showing the asymptotic normality of nonlinear estimators such as GMM estimators. We then establish consistency of various asymptotic variance estimators, including that of Cameron et al. (2011) but also a new estimator that is positive by construction. Next, we show the general consistency, for linear and nonlinear estimators, of the pigeonhole bootstrap, a resampling scheme adapted to multiway clustering. Monte Carlo simulations suggest that inference based on our two preferred methods may be accurate even with very few clusters, and significantly improve upon inference based on Cameron et al. (2011).

Asymptotic results under multiway clustering

2018-07-20 19:33:13

Laurent Davezies, Xavier D'Haultfoeuille, Yannick Guyonvarch

http://arxiv.org/abs/1807.07925v2, http://arxiv.org/pdf/1807.07925v2

econ.EM

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In dynamical framework the conflict between government and the central bank according to the exchange Rate of payment of fixed rates and fixed rates of fixed income (EMU) convergence criteria such that the public debt / GDP ratio The method consists of calculating private public debt management in a public debt management system purpose there is no mechanism to allow naturally for this adjustment.

EMU and ECB Conflicts

2018-07-21 09:57:15

William Mackenzie

http://arxiv.org/abs/1807.08097v1, http://arxiv.org/pdf/1807.08097v1

econ.EM

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Dynamic discrete choice models often discretize the state vector and restrict its dimension in order to achieve valid inference. I propose a novel two-stage estimator for the set-identified structural parameter that incorporates a high-dimensional state space into the dynamic model of imperfect competition. In the first stage, I estimate the state variable's law of motion and the equilibrium policy function using machine learning tools. In the second stage, I plug the first-stage estimates into a moment inequality and solve for the structural parameter. The moment function is presented as the sum of two components, where the first one expresses the equilibrium assumption and the second one is a bias correction term that makes the sum insensitive (i.e., orthogonal) to first-stage bias. The proposed estimator uniformly converges at the root-N rate and I use it to construct confidence regions. The results developed here can be used to incorporate high-dimensional state space into classic dynamic discrete choice models, for example, those considered in Rust (1987), Bajari et al. (2007), and Scott (2013).

Machine Learning for Dynamic Discrete Choice

2018-08-08 01:23:50

Vira Semenova

http://arxiv.org/abs/1808.02569v2, http://arxiv.org/pdf/1808.02569v2

econ.EM

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This paper presents a weighted optimization framework that unifies the binary,multi-valued, continuous, as well as mixture of discrete and continuous treatment, under the unconfounded treatment assignment. With a general loss function, the framework includes the average, quantile and asymmetric least squares causal effect of treatment as special cases. For this general framework, we first derive the semiparametric efficiency bound for the causal effect of treatment, extending the existing bound results to a wider class of models. We then propose a generalized optimization estimation for the causal effect with weights estimated by solving an expanding set of equations. Under some sufficient conditions, we establish consistency and asymptotic normality of the proposed estimator of the causal effect and show that the estimator attains our semiparametric efficiency bound, thereby extending the existing literature on efficient estimation of causal effect to a wider class of applications. Finally, we discuss etimation of some causal effect functionals such as the treatment effect curve and the average outcome. To evaluate the finite sample performance of the proposed procedure, we conduct a small scale simulation study and find that the proposed estimation has practical value. To illustrate the applicability of the procedure, we revisit the literature on campaign advertise and campaign contributions. Unlike the existing procedures which produce mixed results, we find no evidence of campaign advertise on campaign contribution.

A Unified Framework for Efficient Estimation of General Treatment Models

2018-08-15 04:32:29

Chunrong Ai, Oliver Linton, Kaiji Motegi, Zheng Zhang

http://arxiv.org/abs/1808.04936v2, http://arxiv.org/pdf/1808.04936v2

econ.EM

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Recent years have seen many attempts to combine expenditure-side estimates of U.S. real output (GDE) growth with income-side estimates (GDI) to improve estimates of real GDP growth. We show how to incorporate information from multiple releases of noisy data to provide more precise estimates while avoiding some of the identifying assumptions required in earlier work. This relies on a new insight: using multiple data releases allows us to distinguish news and noise measurement errors in situations where a single vintage does not. Our new measure, GDP++, fits the data better than GDP+, the GDP growth measure of Aruoba et al. (2016) published by the Federal Reserve Bank of Philadephia. Historical decompositions show that GDE releases are more informative than GDI, while the use of multiple data releases is particularly important in the quarters leading up to the Great Recession.

Can GDP measurement be further improved? Data revision and reconciliation

2018-08-15 07:48:26

Jan P. A. M. Jacobs, Samad Sarferaz, Jan-Egbert Sturm, Simon van Norden

http://arxiv.org/abs/1808.04970v1, http://arxiv.org/pdf/1808.04970v1

econ.EM

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While investments in renewable energy sources (RES) are incentivized around the world, the policy tools that do so are still poorly understood, leading to costly misadjustments in many cases. As a case study, the deployment dynamics of residential solar photovoltaics (PV) invoked by the German feed-in tariff legislation are investigated. Here we report a model showing that the question of when people invest in residential PV systems is found to be not only determined by profitability, but also by profitability's change compared to the status quo. This finding is interpreted in the light of loss aversion, a concept developed in Kahneman and Tversky's Prospect Theory. The model is able to reproduce most of the dynamics of the uptake with only a few financial and behavioral assumptions

When Do Households Invest in Solar Photovoltaics? An Application of Prospect Theory

2018-08-16 19:29:55

Martin Klein, Marc Deissenroth

http://dx.doi.org/10.1016/j.enpol.2017.06.067, http://arxiv.org/abs/1808.05572v1, http://arxiv.org/pdf/1808.05572v1

econ.EM

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Kitamura and Stoye (2014) develop a nonparametric test for linear inequality constraints, when these are are represented as vertices of a polyhedron instead of its faces. They implement this test for an application to nonparametric tests of Random Utility Models. As they note in their paper, testing such models is computationally challenging. In this paper, we develop and implement more efficient algorithms, based on column generation, to carry out the test. These improved algorithms allow us to tackle larger datasets.

Column Generation Algorithms for Nonparametric Analysis of Random Utility Models

2018-12-04 16:28:33

Bart Smeulders

http://arxiv.org/abs/1812.01400v1, http://arxiv.org/pdf/1812.01400v1

econ.EM

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The purpose of this paper is to provide guidelines for empirical researchers who use a class of bivariate threshold crossing models with dummy endogenous variables. A common practice employed by the researchers is the specification of the joint distribution of the unobservables as a bivariate normal distribution, which results in a bivariate probit model. To address the problem of misspecification in this practice, we propose an easy-to-implement semiparametric estimation framework with parametric copula and nonparametric marginal distributions. We establish asymptotic theory, including root-n normality, for the sieve maximum likelihood estimators that can be used to conduct inference on the individual structural parameters and the average treatment effect (ATE). In order to show the practical relevance of the proposed framework, we conduct a sensitivity analysis via extensive Monte Carlo simulation exercises. The results suggest that the estimates of the parameters, especially the ATE, are sensitive to parametric specification, while semiparametric estimation exhibits robustness to underlying data generating processes. We then provide an empirical illustration where we estimate the effect of health insurance on doctor visits. In this paper, we also show that the absence of excluded instruments may result in identification failure, in contrast to what some practitioners believe.

Estimation in a Generalization of Bivariate Probit Models with Dummy Endogenous Regressors

2018-08-17 11:34:04

Sukjin Han, Sungwon Lee

http://arxiv.org/abs/1808.05792v2, http://arxiv.org/pdf/1808.05792v2

econ.EM

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Under suitable conditions, one-step generalized method of moments (GMM) based on the first-difference (FD) transformation is numerically equal to one-step GMM based on the forward orthogonal deviations (FOD) transformation. However, when the number of time periods ($T$) is not small, the FOD transformation requires less computational work. This paper shows that the computational complexity of the FD and FOD transformations increases with the number of individuals ($N$) linearly, but the computational complexity of the FOD transformation increases with $T$ at the rate $T^{4}$ increases, while the computational complexity of the FD transformation increases at the rate $T^{6}$ increases. Simulations illustrate that calculations exploiting the FOD transformation are performed orders of magnitude faster than those using the FD transformation. The results in the paper indicate that, when one-step GMM based on the FD and FOD transformations are the same, Monte Carlo experiments can be conducted much faster if the FOD version of the estimator is used.

Quantifying the Computational Advantage of Forward Orthogonal Deviations

2018-08-17 23:57:31

Robert F. Phillips

http://arxiv.org/abs/1808.05995v1, http://arxiv.org/pdf/1808.05995v1

econ.EM

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There is generally a need to deal with quality change and new goods in the consumer price index due to the underlying dynamic item universe. Traditionally axiomatic tests are defined for a fixed universe. We propose five tests explicitly formulated for a dynamic item universe, and motivate them both from the perspectives of a cost-of-goods index and a cost-of-living index. None of the indices satisfies all the tests at the same time, which are currently available for making use of scanner data that comprises the whole item universe. The set of tests provides a rigorous diagnostic for whether an index is completely appropriate in a dynamic item universe, as well as pointing towards the directions of possible remedies. We thus outline a large index family that potentially can satisfy all the tests.

Tests for price indices in a dynamic item universe

2018-08-27 22:01:08

Li-Chun Zhang, Ingvild Johansen, Ragnhild Nygaard

http://arxiv.org/abs/1808.08995v2, http://arxiv.org/pdf/1808.08995v2

econ.EM

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A fixed-design residual bootstrap method is proposed for the two-step estimator of Francq and Zako\"ian (2015) associated with the conditional Value-at-Risk. The bootstrap's consistency is proven for a general class of volatility models and intervals are constructed for the conditional Value-at-Risk. A simulation study reveals that the equal-tailed percentile bootstrap interval tends to fall short of its nominal value. In contrast, the reversed-tails bootstrap interval yields accurate coverage. We also compare the theoretically analyzed fixed-design bootstrap with the recursive-design bootstrap. It turns out that the fixed-design bootstrap performs equally well in terms of average coverage, yet leads on average to shorter intervals in smaller samples. An empirical application illustrates the interval estimation.

A Residual Bootstrap for Conditional Value-at-Risk

2018-08-28 08:34:36

Eric Beutner, Alexander Heinemann, Stephan Smeekes

http://arxiv.org/abs/1808.09125v4, http://arxiv.org/pdf/1808.09125v4

econ.EM

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Kotlarski's identity has been widely used in applied economic research. However, how to conduct inference based on this popular identification approach has been an open question for two decades. This paper addresses this open problem by constructing a novel confidence band for the density function of a latent variable in repeated measurement error model. The confidence band builds on our finding that we can rewrite Kotlarski's identity as a system of linear moment restrictions. The confidence band controls the asymptotic size uniformly over a class of data generating processes, and it is consistent against all fixed alternatives. Simulation studies support our theoretical results.

Inference based on Kotlarski's Identity

2018-08-28 18:54:59

Kengo Kato, Yuya Sasaki, Takuya Ura

http://arxiv.org/abs/1808.09375v3, http://arxiv.org/pdf/1808.09375v3

econ.EM

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This study considers various semiparametric difference-in-differences models under different assumptions on the relation between the treatment group identifier, time and covariates for cross-sectional and panel data. The variance lower bound is shown to be sensitive to the model assumptions imposed implying a robustness-efficiency trade-off. The obtained efficient influence functions lead to estimators that are rate double robust and have desirable asymptotic properties under weak first stage convergence conditions. This enables to use sophisticated machine-learning algorithms that can cope with settings where common trend confounding is high-dimensional. The usefulness of the proposed estimators is assessed in an empirical example. It is shown that the efficiency-robustness trade-offs and the choice of first stage predictors can lead to divergent empirical results in practice.

Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding

2018-09-05 20:41:34

Michael Zimmert

http://arxiv.org/abs/1809.01643v5, http://arxiv.org/pdf/1809.01643v5

econ.EM

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We study estimation, pointwise and simultaneous inference, and confidence intervals for many average partial effects of lasso Logit. Focusing on high-dimensional, cluster-sampling environments, we propose a new average partial effect estimator and explore its asymptotic properties. Practical penalty choices compatible with our asymptotic theory are also provided. The proposed estimator allow for valid inference without requiring oracle property. We provide easy-to-implement algorithms for cluster-robust high-dimensional hypothesis testing and construction of simultaneously valid confidence intervals using a multiplier cluster bootstrap. We apply the proposed algorithms to the text regression model of Wu (2018) to examine the presence of gendered language on the internet.

Many Average Partial Effects: with An Application to Text Regression

2018-12-22 01:35:51

Harold D. Chiang

http://arxiv.org/abs/1812.09397v5, http://arxiv.org/pdf/1812.09397v5

econ.EM

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The bootstrap is a method for estimating the distribution of an estimator or test statistic by re-sampling the data or a model estimated from the data. Under conditions that hold in a wide variety of econometric applications, the bootstrap provides approximations to distributions of statistics, coverage probabilities of confidence intervals, and rejection probabilities of hypothesis tests that are more accurate than the approximations of first-order asymptotic distribution theory. The reductions in the differences between true and nominal coverage or rejection probabilities can be very large. In addition, the bootstrap provides a way to carry out inference in certain settings where obtaining analytic distributional approximations is difficult or impossible. This article explains the usefulness and limitations of the bootstrap in contexts of interest in econometrics. The presentation is informal and expository. It provides an intuitive understanding of how the bootstrap works. Mathematical details are available in references that are cited.

Bootstrap Methods in Econometrics

2018-09-11 19:39:03

Joel L. Horowitz

http://arxiv.org/abs/1809.04016v1, http://arxiv.org/pdf/1809.04016v1

econ.EM

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A method for implicit variable selection in mixture of experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.

Bayesian shrinkage in mixture of experts models: Identifying robust determinants of class membership

2018-09-13 12:30:21

Gregor Zens

http://arxiv.org/abs/1809.04853v2, http://arxiv.org/pdf/1809.04853v2

econ.EM

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Time averaging has been the traditional approach to handle mixed sampling frequencies. However, it ignores information possibly embedded in high frequency. Mixed data sampling (MIDAS) regression models provide a concise way to utilize the additional information in high-frequency variables. In this paper, we propose a specification test to choose between time averaging and MIDAS models, based on a Durbin-Wu-Hausman test. In particular, a set of instrumental variables is proposed and theoretically validated when the frequency ratio is large. As a result, our method tends to be more powerful than existing methods, as reconfirmed through the simulations.

On the Choice of Instruments in Mixed Frequency Specification Tests

2018-09-14 19:59:44

Yun Liu, Yeonwoo Rho

http://arxiv.org/abs/1809.05503v1, http://arxiv.org/pdf/1809.05503v1

econ.EM

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This article deals with asimple issue: if we have grouped data with a binary dependent variable and want to include fixed effects (group specific intercepts) in the specification, is Ordinary Least Squares (OLS) in any way superior to a (conditional) logit form? In particular, what are the consequences of using OLS instead of a fixed effects logit model with respect to the latter dropping all units which show no variability in the dependent variable while the former allows for estimation using all units. First, we show that the discussion of fthe incidental parameters problem is based on an assumption about the kinds of data being studied; for what appears to be the common use of fixed effect models in political science the incidental parameters issue is illusory. Turning to linear models, we see that OLS yields a linear combination of the estimates for the units with and without variation in the dependent variable, and so the coefficient estimates must be carefully interpreted. The article then compares two methods of estimating logit models with fixed effects, and shows that the Chamberlain conditional logit is as good as or better than a logit analysis which simply includes group specific intercepts (even though the conditional logit technique was designed to deal with the incidental parameters problem!). Related to this, the article discusses the estimation of marginal effects using both OLS and logit. While it appears that a form of logit with fixed effects can be used to estimate marginal effects, this method can be improved by starting with conditional logit and then using the those parameter estimates to constrain the logit with fixed effects model. This method produces estimates of sample average marginal effects that are at least as good as OLS, and much better when group size is small or the number of groups is large. .

Estimating grouped data models with a binary dependent variable and fixed effects: What are the issues

2018-09-18 05:25:25

Nathaniel Beck

http://arxiv.org/abs/1809.06505v1, http://arxiv.org/pdf/1809.06505v1

econ.EM

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We provide new results for nonparametric identification, estimation, and inference of causal effects using `proxy controls': observables that are noisy but informative proxies for unobserved confounding factors. Our analysis applies to cross-sectional settings but is particularly well-suited to panel models. Our identification results motivate a simple and `well-posed' nonparametric estimator. We derive convergence rates for the estimator and construct uniform confidence bands with asymptotically correct size. In panel settings, our methods provide a novel approach to the difficult problem of identification with non-separable, general heterogeneity and fixed $T$. In panels, observations from different periods serve as proxies for unobserved heterogeneity and our key identifying assumptions follow from restrictions on the serial dependence structure. We apply our methods to two empirical settings. We estimate consumer demand counterfactuals using panel data and we estimate causal effects of grade retention on cognitive performance.

Proxy Controls and Panel Data

2018-09-30 03:38:11

Ben Deaner

http://arxiv.org/abs/1810.00283v8, http://arxiv.org/pdf/1810.00283v8

econ.EM

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We consider the problem of regression with selectively observed covariates in a nonparametric framework. Our approach relies on instrumental variables that explain variation in the latent covariates but have no direct effect on selection. The regression function of interest is shown to be a weighted version of observed conditional expectation where the weighting function is a fraction of selection probabilities. Nonparametric identification of the fractional probability weight (FPW) function is achieved via a partial completeness assumption. We provide primitive functional form assumptions for partial completeness to hold. The identification result is constructive for the FPW series estimator. We derive the rate of convergence and also the pointwise asymptotic distribution. In both cases, the asymptotic performance of the FPW series estimator does not suffer from the inverse problem which derives from the nonparametric instrumental variable approach. In a Monte Carlo study, we analyze the finite sample properties of our estimator and we compare our approach to inverse probability weighting, which can be used alternatively for unconditional moment estimation. In the empirical application, we focus on two different applications. We estimate the association between income and health using linked data from the SHARE survey and administrative pension information and use pension entitlements as an instrument. In the second application we revisit the question how income affects the demand for housing based on data from the German Socio-Economic Panel Study (SOEP). In this application we use regional income information on the residential block level as an instrument. In both applications we show that income is selectively missing and we demonstrate that standard methods that do not account for the nonrandom selection process lead to significantly biased estimates for individuals with low income.

Nonparametric Regression with Selectively Missing Covariates

2018-09-30 18:52:54

Christoph Breunig, Peter Haan

http://arxiv.org/abs/1810.00411v4, http://arxiv.org/pdf/1810.00411v4

econ.EM

em

The intention of this paper is to discuss the mathematical model of causality introduced by C.W.J. Granger in 1969. The Granger's model of causality has become well-known and often used in various econometric models describing causal systems, e.g., between commodity prices and exchange rates. Our paper presents a new mathematical model of causality between two measured objects. We have slightly modified the well-known Kolmogorovian probability model. In particular, we use the horizontal sum of set $\sigma$-algebras instead of their direct product.

Granger causality on horizontal sum of Boolean algebras

2018-10-03 12:27:43

M. Bohdalová, M. Kalina, O. Nánásiová

http://arxiv.org/abs/1810.01654v1, http://arxiv.org/pdf/1810.01654v1

econ.EM

em

Explanatory variables in a predictive regression typically exhibit low signal strength and various degrees of persistence. Variable selection in such a context is of great importance. In this paper, we explore the pitfalls and possibilities of the LASSO methods in this predictive regression framework. In the presence of stationary, local unit root, and cointegrated predictors, we show that the adaptive LASSO cannot asymptotically eliminate all cointegrating variables with zero regression coefficients. This new finding motivates a novel post-selection adaptive LASSO, which we call the twin adaptive LASSO (TAlasso), to restore variable selection consistency. Accommodating the system of heterogeneous regressors, TAlasso achieves the well-known oracle property. In contrast, conventional LASSO fails to attain coefficient estimation consistency and variable screening in all components simultaneously. We apply these LASSO methods to evaluate the short- and long-horizon predictability of S\&P 500 excess returns.

On LASSO for Predictive Regression

2018-10-07 16:19:07

Ji Hyung Lee, Zhentao Shi, Zhan Gao

http://arxiv.org/abs/1810.03140v4, http://arxiv.org/pdf/1810.03140v4

econ.EM

em

This paper proposes a new approach to obtain uniformly valid inference for linear functionals or scalar subvectors of a partially identified parameter defined by linear moment inequalities. The procedure amounts to bootstrapping the value functions of randomly perturbed linear programming problems, and does not require the researcher to grid over the parameter space. The low-level conditions for uniform validity rely on genericity results for linear programs. The unconventional perturbation approach produces a confidence set with a coverage probability of 1 over the identified set, but obtains exact coverage on an outer set, is valid under weak assumptions, and is computationally simple to implement.

Simple Inference on Functionals of Set-Identified Parameters Defined by Linear Moments

2018-10-07 20:03:14

JoonHwan Cho, Thomas M. Russell

http://arxiv.org/abs/1810.03180v10, http://arxiv.org/pdf/1810.03180v10

econ.EM

em

In this paper we consider the properties of the Pesaran (2004, 2015a) CD test for cross-section correlation when applied to residuals obtained from panel data models with many estimated parameters. We show that the presence of period-specific parameters leads the CD test statistic to diverge as length of the time dimension of the sample grows. This result holds even if cross-section dependence is correctly accounted for and hence constitutes an example of the Incidental Parameters Problem. The relevance of this problem is investigated both for the classical Time Fixed Effects estimator as well as the Common Correlated Effects estimator of Pesaran (2006). We suggest a weighted CD test statistic which re-establishes standard normal inference under the null hypothesis. Given the widespread use of the CD test statistic to test for remaining cross-section correlation, our results have far reaching implications for empirical researchers.

The Incidental Parameters Problem in Testing for Remaining Cross-section Correlation

2018-10-09 00:48:52

Arturas Juodis, Simon Reese

http://arxiv.org/abs/1810.03715v4, http://arxiv.org/pdf/1810.03715v4

econ.EM

em

This paper studies nonparametric identification and counterfactual bounds for heterogeneous firms that can be ranked in terms of productivity. Our approach works when quantities and prices are latent, rendering standard approaches inapplicable. Instead, we require observation of profits or other optimizing-values such as costs or revenues, and either prices or price proxies of flexibly chosen variables. We extend classical duality results for price-taking firms to a setup with discrete heterogeneity, endogeneity, and limited variation in possibly latent prices. Finally, we show that convergence results for nonparametric estimators may be directly converted to convergence results for production sets.

Prices, Profits, Proxies, and Production

2018-10-10 21:15:29

Victor H. Aguiar, Nail Kashaev, Roy Allen

http://arxiv.org/abs/1810.04697v4, http://arxiv.org/pdf/1810.04697v4

econ.EM

em

A long-standing question about consumer behavior is whether individuals' observed purchase decisions satisfy the revealed preference (RP) axioms of the utility maximization theory (UMT). Researchers using survey or experimental panel data sets on prices and consumption to answer this question face the well-known problem of measurement error. We show that ignoring measurement error in the RP approach may lead to overrejection of the UMT. To solve this problem, we propose a new statistical RP framework for consumption panel data sets that allows for testing the UMT in the presence of measurement error. Our test is applicable to all consumer models that can be characterized by their first-order conditions. Our approach is nonparametric, allows for unrestricted heterogeneity in preferences, and requires only a centering condition on measurement error. We develop two applications that provide new evidence about the UMT. First, we find support in a survey data set for the dynamic and time-consistent UMT in single-individual households, in the presence of \emph{nonclassical} measurement error in consumption. In the second application, we cannot reject the static UMT in a widely used experimental data set in which measurement error in prices is assumed to be the result of price misperception due to the experimental design. The first finding stands in contrast to the conclusions drawn from the deterministic RP test of Browning (1989). The second finding reverses the conclusions drawn from the deterministic RP test of Afriat (1967) and Varian (1982).

Stochastic Revealed Preferences with Measurement Error

2018-10-12 02:25:24

Victor H. Aguiar, Nail Kashaev

http://arxiv.org/abs/1810.05287v2, http://arxiv.org/pdf/1810.05287v2

econ.EM

em

In this paper, we study estimation of nonlinear models with cross sectional data using two-step generalized estimating equations (GEE) in the quasi-maximum likelihood estimation (QMLE) framework. In the interest of improving efficiency, we propose a grouping estimator to account for the potential spatial correlation in the underlying innovations. We use a Poisson model and a Negative Binomial II model for count data and a Probit model for binary response data to demonstrate the GEE procedure. Under mild weak dependency assumptions, results on estimation consistency and asymptotic normality are provided. Monte Carlo simulations show efficiency gain of our approach in comparison of different estimation methods for count data and binary response data. Finally we apply the GEE approach to study the determinants of the inflow foreign direct investment (FDI) to China.

Using generalized estimating equations to estimate nonlinear models with spatial data

2018-10-13 15:58:41

Cuicui Lu, Weining Wang, Jeffrey M. Wooldridge

http://arxiv.org/abs/1810.05855v1, http://arxiv.org/pdf/1810.05855v1

econ.EM

em

Nonparametric Instrumental Variables (NPIV) analysis is based on a conditional moment restriction. We show that if this moment condition is even slightly misspecified, say because instruments are not quite valid, then NPIV estimates can be subject to substantial asymptotic error and the identified set under a relaxed moment condition may be large. Imposing strong a priori smoothness restrictions mitigates the problem but induces bias if the restrictions are too strong. In order to manage this trade-off we develop a methods for empirical sensitivity analysis and apply them to the consumer demand data previously analyzed in Blundell (2007) and Horowitz (2011).

Nonparametric Instrumental Variables Estimation Under Misspecification

2019-01-04 21:52:59

Ben Deaner

http://arxiv.org/abs/1901.01241v7, http://arxiv.org/pdf/1901.01241v7

econ.EM

em

This paper develops a consistent heteroskedasticity robust Lagrange Multiplier (LM) type specification test for semiparametric conditional mean models. Consistency is achieved by turning a conditional moment restriction into a growing number of unconditional moment restrictions using series methods. The proposed test statistic is straightforward to compute and is asymptotically standard normal under the null. Compared with the earlier literature on series-based specification tests in parametric models, I rely on the projection property of series estimators and derive a different normalization of the test statistic. Compared with the recent test in Gupta (2018), I use a different way of accounting for heteroskedasticity. I demonstrate using Monte Carlo studies that my test has superior finite sample performance compared with the existing tests. I apply the test to one of the semiparametric gasoline demand specifications from Yatchew and No (2001) and find no evidence against it.

A Consistent Heteroskedasticity Robust LM Type Specification Test for Semiparametric Models

2018-10-17 18:37:02

Ivan Korolev

http://arxiv.org/abs/1810.07620v3, http://arxiv.org/pdf/1810.07620v3

econ.EM

em

This study considers treatment effect models in which others' treatment decisions can affect both one's own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one's own treatment and that from the partner are identifiable on the conditional supports of certain threshold variables determined through the game model. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. We show that the proposed estimator is uniformly consistent and asymptotically normally distributed. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance.

Treatment Effect Models with Strategic Interaction in Treatment Decisions

2018-10-19 06:51:42

Tadao Hoshino, Takahide Yanagi

http://arxiv.org/abs/1810.08350v11, http://arxiv.org/pdf/1810.08350v11

econ.EM

em

In this paper we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price, hence we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by OLS and elastic net and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component on a mean reverting jump diffusion model in the residuals. The models' forecasts are evaluated using four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included or not. In the results it is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure in order to properly forecast correlated time series.

Probabilistic Forecasting in Day-Ahead Electricity Markets: Simulating Peak and Off-Peak Prices

2018-10-19 12:27:16

Peru Muniain, Florian Ziel

http://dx.doi.org/10.1016/j.ijforecast.2019.11.006, http://arxiv.org/abs/1810.08418v2, http://arxiv.org/pdf/1810.08418v2

econ.EM

em

We propose a novel two-regime regression model where regime switching is driven by a vector of possibly unobservable factors. When the factors are latent, we estimate them by the principal component analysis of a panel data set. We show that the optimization problem can be reformulated as mixed integer optimization, and we present two alternative computational algorithms. We derive the asymptotic distribution of the resulting estimator under the scheme that the threshold effect shrinks to zero. In particular, we establish a phase transition that describes the effect of first-stage factor estimation as the cross-sectional dimension of panel data increases relative to the time-series dimension. Moreover, we develop bootstrap inference and illustrate our methods via numerical studies.

Factor-Driven Two-Regime Regression

2018-10-26 00:12:52

Sokbae Lee, Yuan Liao, Myung Hwan Seo, Youngki Shin

http://dx.doi.org/10.1214/20-AOS2017, http://arxiv.org/abs/1810.11109v4, http://arxiv.org/pdf/1810.11109v4

econ.EM

em

Let Y be an outcome of interest, X a vector of treatment measures, and W a vector of pre-treatment control variables. Here X may include (combinations of) continuous, discrete, and/or non-mutually exclusive "treatments". Consider the linear regression of Y onto X in a subpopulation homogenous in W = w (formally a conditional linear predictor). Let b0(w) be the coefficient vector on X in this regression. We introduce a semiparametrically efficient estimate of the average beta0 = E[b0(W)]. When X is binary-valued (multi-valued) our procedure recovers the (a vector of) average treatment effect(s). When X is continuously-valued, or consists of multiple non-exclusive treatments, our estimand coincides with the average partial effect (APE) of X on Y when the underlying potential response function is linear in X, but otherwise heterogenous across agents. When the potential response function takes a general nonlinear/heterogenous form, and X is continuously-valued, our procedure recovers a weighted average of the gradient of this response across individuals and values of X. We provide a simple, and semiparametrically efficient, method of covariate adjustment for settings with complicated treatment regimes. Our method generalizes familiar methods of covariate adjustment used for program evaluation as well as methods of semiparametric regression (e.g., the partially linear regression model).

Semiparametrically efficient estimation of the average linear regression function

2018-10-30 06:26:33

Bryan S. Graham, Cristine Campos de Xavier Pinto

http://arxiv.org/abs/1810.12511v1, http://arxiv.org/pdf/1810.12511v1

econ.EM

em

We investigate the finite sample performance of causal machine learning estimators for heterogeneous causal effects at different aggregation levels. We employ an Empirical Monte Carlo Study that relies on arguably realistic data generation processes (DGPs) based on actual data. We consider 24 different DGPs, eleven different causal machine learning estimators, and three aggregation levels of the estimated effects. In the main DGPs, we allow for selection into treatment based on a rich set of observable covariates. We provide evidence that the estimators can be categorized into three groups. The first group performs consistently well across all DGPs and aggregation levels. These estimators have multiple steps to account for the selection into the treatment and the outcome process. The second group shows competitive performance only for particular DGPs. The third group is clearly outperformed by the other estimators.

Machine Learning Estimation of Heterogeneous Causal Effects: Empirical Monte Carlo Evidence

2018-10-31 15:10:25

Michael C. Knaus, Michael Lechner, Anthony Strittmatter

http://dx.doi.org/10.1093/ectj/utaa014, http://arxiv.org/abs/1810.13237v2, http://arxiv.org/pdf/1810.13237v2

econ.EM

em

This article proposes doubly robust estimators for the average treatment effect on the treated (ATT) in difference-in-differences (DID) research designs. In contrast to alternative DID estimators, the proposed estimators are consistent if either (but not necessarily both) a propensity score or outcome regression working models are correctly specified. We also derive the semiparametric efficiency bound for the ATT in DID designs when either panel or repeated cross-section data are available, and show that our proposed estimators attain the semiparametric efficiency bound when the working models are correctly specified. Furthermore, we quantify the potential efficiency gains of having access to panel data instead of repeated cross-section data. Finally, by paying articular attention to the estimation method used to estimate the nuisance parameters, we show that one can sometimes construct doubly robust DID estimators for the ATT that are also doubly robust for inference. Simulation studies and an empirical application illustrate the desirable finite-sample performance of the proposed estimators. Open-source software for implementing the proposed policy evaluation tools is available.

Doubly Robust Difference-in-Differences Estimators

2018-11-30 00:18:26

Pedro H. C. Sant'Anna, Jun B. Zhao

http://arxiv.org/abs/1812.01723v3, http://arxiv.org/pdf/1812.01723v3

econ.EM

em

This paper examines a commonly used measure of persuasion whose precise interpretation has been obscure in the literature. By using the potential outcome framework, we define the causal persuasion rate by a proper conditional probability of taking the action of interest with a persuasive message conditional on not taking the action without the message. We then formally study identification under empirically relevant data scenarios and show that the commonly adopted measure generally does not estimate, but often overstates, the causal rate of persuasion. We discuss several new parameters of interest and provide practical methods for causal inference.

Identifying the Effect of Persuasion

2018-12-06 03:20:35

Sung Jae Jun, Sokbae Lee

http://arxiv.org/abs/1812.02276v6, http://arxiv.org/pdf/1812.02276v6

econ.EM

em

We develop a uniform test for detecting and dating explosive behavior of a strictly stationary GARCH$(r,s)$ (generalized autoregressive conditional heteroskedasticity) process. Namely, we test the null hypothesis of a globally stable GARCH process with constant parameters against an alternative where there is an 'abnormal' period with changed parameter values. During this period, the change may lead to an explosive behavior of the volatility process. It is assumed that both the magnitude and the timing of the breaks are unknown. We develop a double supreme test for the existence of a break, and then provide an algorithm to identify the period of change. Our theoretical results hold under mild moment assumptions on the innovations of the GARCH process. Technically, the existing properties for the QMLE in the GARCH model need to be reinvestigated to hold uniformly over all possible periods of change. The key results involve a uniform weak Bahadur representation for the estimated parameters, which leads to weak convergence of the test statistic to the supreme of a Gaussian Process. In simulations we show that the test has good size and power for reasonably large time series lengths. We apply the test to Apple asset returns and Bitcoin returns.

A supreme test for periodic explosive GARCH

2018-12-09 15:51:14

Stefan Richter, Weining Wang, Wei Biao Wu

http://arxiv.org/abs/1812.03475v1, http://arxiv.org/pdf/1812.03475v1

econ.EM

em

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.

What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

2018-12-16 23:24:02

Anthony Strittmatter

http://arxiv.org/abs/1812.06533v3, http://arxiv.org/pdf/1812.06533v3

econ.EM

em

This paper explores the use of a fuzzy regression discontinuity design where multiple treatments are applied at the threshold. The identification results show that, under the very strong assumption that the change in the probability of treatment at the cutoff is equal across treatments, a difference-in-discontinuities estimator identifies the treatment effect of interest. The point estimates of the treatment effect using a simple fuzzy difference-in-discontinuities design are biased if the change in the probability of a treatment applying at the cutoff differs across treatments. Modifications of the fuzzy difference-in-discontinuities approach that rely on milder assumptions are also proposed. Our results suggest caution is needed when applying before-and-after methods in the presence of fuzzy discontinuities. Using data from the National Health Interview Survey, we apply this new identification strategy to evaluate the causal effect of the Affordable Care Act (ACA) on older Americans' health care access and utilization.

Fuzzy Difference-in-Discontinuities: Identification Theory and Application to the Affordable Care Act

2018-12-17 00:27:54

Hector Galindo-Silva, Nibene Habib Some, Guy Tchuente

http://arxiv.org/abs/1812.06537v3, http://arxiv.org/pdf/1812.06537v3

econ.EM

em

We propose convenient inferential methods for potentially nonstationary multivariate unobserved components models with fractional integration and cointegration. Based on finite-order ARMA approximations in the state space representation, maximum likelihood estimation can make use of the EM algorithm and related techniques. The approximation outperforms the frequently used autoregressive or moving average truncation, both in terms of computational costs and with respect to approximation quality. Monte Carlo simulations reveal good estimation properties of the proposed methods for processes of different complexity and dimension.

Approximate State Space Modelling of Unobserved Fractional Components

2018-12-21 17:25:45

Tobias Hartl, Roland Weigand

http://dx.doi.org/10.1080/07474938.2020.1841444, http://arxiv.org/abs/1812.09142v3, http://arxiv.org/pdf/1812.09142v3

econ.EM

em

We propose a setup for fractionally cointegrated time series which is formulated in terms of latent integrated and short-memory components. It accommodates nonstationary processes with different fractional orders and cointegration of different strengths and is applicable in high-dimensional settings. In an application to realized covariance matrices, we find that orthogonal short- and long-memory components provide a reasonable fit and competitive out-of-sample performance compared to several competing methods.

Multivariate Fractional Components Analysis

2018-12-21 17:33:27

Tobias Hartl, Roland Weigand

http://arxiv.org/abs/1812.09149v2, http://arxiv.org/pdf/1812.09149v2

econ.EM

em

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is restrictive, because a cautious decision maker may prefer to target a robust location measure such as a quantile or a trimmed mean. Furthermore, socio-economic decision making often requires targeting purpose specific characteristics of the outcome distribution, such as its inherent degree of inequality, welfare or poverty. In the present paper we introduce and study sequential learning algorithms when the distributional characteristic of interest is a general functional of the outcome distribution. Minimax expected regret optimality results are obtained within the subclass of explore-then-commit policies, and for the unrestricted class of all policies.

Functional Sequential Treatment Allocation

2018-12-22 02:18:13

Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev

http://arxiv.org/abs/1812.09408v8, http://arxiv.org/pdf/1812.09408v8

econ.EM

em

In many applications common in testing for convergence the number of cross-sectional units is large and the number of time periods are few. In these situations asymptotic tests based on an omnibus null hypothesis are characterised by a number of problems. In this paper we propose a multiple pairwise comparisons method based on an a recursive bootstrap to test for convergence with no prior information on the composition of convergence clubs. Monte Carlo simulations suggest that our bootstrap-based test performs well to correctly identify convergence clubs when compared with other similar tests that rely on asymptotic arguments. Across a potentially large number of regions, using both cross-country and regional data for the European Union, we find that the size distortion which afflicts standard tests and results in a bias towards finding less convergence, is ameliorated when we utilise our bootstrap test.

Robust Tests for Convergence Clubs

2018-12-22 15:11:04

Luisa Corrado, Melvyn Weeks, Thanasis Stengos, M. Ege Yazgan

http://arxiv.org/abs/1812.09518v1, http://arxiv.org/pdf/1812.09518v1

econ.EM

em

We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a $K$-fold cross-fitting procedure for bias-correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized $t$-statistic, which has an asymptotically pivotal $t$-distribution. Our $t$-test is easy to implement, provably robust against misspecification, valid with non-stationary data, and demonstrates an excellent small sample performance. Compared to difference-in-differences, our method often yields more than 50% shorter confidence intervals and is robust to violations of parallel trends assumptions. An $\texttt{R}$-package for implementing our methods is available.

A $t$-test for synthetic controls

2018-12-27 23:40:13

Victor Chernozhukov, Kaspar Wuthrich, Yinchu Zhu

http://arxiv.org/abs/1812.10820v7, http://arxiv.org/pdf/1812.10820v7

econ.EM

em

The instrumental variable quantile regression (IVQR) model (Chernozhukov and Hansen, 2005) is a popular tool for estimating causal quantile effects with endogenous covariates. However, estimation is complicated by the non-smoothness and non-convexity of the IVQR GMM objective function. This paper shows that the IVQR estimation problem can be decomposed into a set of conventional quantile regression sub-problems which are convex and can be solved efficiently. This reformulation leads to new identification results and to fast, easy to implement, and tuning-free estimators that do not require the availability of high-level "black box" optimization routines.

Decentralization Estimators for Instrumental Variable Quantile Regression Models

2018-12-28 11:50:33

Hiroaki Kaido, Kaspar Wuthrich

http://arxiv.org/abs/1812.10925v4, http://arxiv.org/pdf/1812.10925v4

econ.EM

em

Predicting future successful designs and corresponding market opportunity is a fundamental goal of product design firms. There is accordingly a long history of quantitative approaches that aim to capture diverse consumer preferences, and then translate those preferences to corresponding "design gaps" in the market. We extend this work by developing a deep learning approach to predict design gaps in the market. These design gaps represent clusters of designs that do not yet exist, but are predicted to be both (1) highly preferred by consumers, and (2) feasible to build under engineering and manufacturing constraints. This approach is tested on the entire U.S. automotive market using of millions of real purchase data. We retroactively predict design gaps in the market, and compare predicted design gaps with actual known successful designs. Our preliminary results give evidence it may be possible to predict design gaps, suggesting this approach has promise for early identification of market opportunity.

Predicting "Design Gaps" in the Market: Deep Consumer Choice Models under Probabilistic Design Constraints

2018-12-28 18:56:46

Alex Burnap, John Hauser

http://arxiv.org/abs/1812.11067v1, http://arxiv.org/pdf/1812.11067v1

econ.EM

em

This paper studies identification and estimation of a class of dynamic models in which the decision maker (DM) is uncertain about the data-generating process. The DM surrounds a benchmark model that he or she fears is misspecified by a set of models. Decisions are evaluated under a worst-case model delivering the lowest utility among all models in this set. The DM's benchmark model and preference parameters are jointly underidentified. With the benchmark model held fixed, primitive conditions are established for identification of the DM's worst-case model and preference parameters. The key step in the identification analysis is to establish existence and uniqueness of the DM's continuation value function allowing for unbounded statespace and unbounded utilities. To do so, fixed-point results are derived for monotone, convex operators that act on a Banach space of thin-tailed functions arising naturally from the structure of the continuation value recursion. The fixed-point results are quite general; applications to models with learning and Rust-type dynamic discrete choice models are also discussed. For estimation, a perturbation result is derived which provides a necessary and sufficient condition for consistent estimation of continuation values and the worst-case model. The result also allows convergence rates of estimators to be characterized. An empirical application studies an endowment economy where the DM's benchmark model may be interpreted as an aggregate of experts' forecasting models. The application reveals time-variation in the way the DM pessimistically distorts benchmark probabilities. Consequences for asset pricing are explored and connections are drawn with the literature on macroeconomic uncertainty.

Dynamic Models with Robust Decision Makers: Identification and Estimation

2018-12-29 02:36:41

Timothy M. Christensen

http://arxiv.org/abs/1812.11246v3, http://arxiv.org/pdf/1812.11246v3

econ.EM

em

This paper introduces a flexible regularization approach that reduces point estimation risk of group means stemming from e.g. categorical regressors, (quasi-)experimental data or panel data models. The loss function is penalized by adding weighted squared l2-norm differences between group location parameters and informative first-stage estimates. Under quadratic loss, the penalized estimation problem has a simple interpretable closed-form solution that nests methods established in the literature on ridge regression, discretized support smoothing kernels and model averaging methods. We derive risk-optimal penalty parameters and propose a plug-in approach for estimation. The large sample properties are analyzed in an asymptotic local to zero framework by introducing a class of sequences for close and distant systems of locations that is sufficient for describing a large range of data generating processes. We provide the asymptotic distributions of the shrinkage estimators under different penalization schemes. The proposed plug-in estimator uniformly dominates the ordinary least squares in terms of asymptotic risk if the number of groups is larger than three. Monte Carlo simulations reveal robust improvements over standard methods in finite samples. Real data examples of estimating time trends in a panel and a difference-in-differences study illustrate potential applications.

Shrinkage for Categorical Regressors

2019-01-07 19:17:23

Phillip Heiler, Jana Mareckova

http://arxiv.org/abs/1901.01898v1, http://arxiv.org/pdf/1901.01898v1

econ.EM

em

This article introduces lassopack, a suite of programs for regularized regression in Stata. lassopack implements lasso, square-root lasso, elastic net, ridge regression, adaptive lasso and post-estimation OLS. The methods are suitable for the high-dimensional setting where the number of predictors $p$ may be large and possibly greater than the number of observations, $n$. We offer three different approaches for selecting the penalization (`tuning') parameters: information criteria (implemented in lasso2), $K$-fold cross-validation and $h$-step ahead rolling cross-validation for cross-section, panel and time-series data (cvlasso), and theory-driven (`rigorous') penalization for the lasso and square-root lasso for cross-section and panel data (rlasso). We discuss the theoretical framework and practical considerations for each approach. We also present Monte Carlo results to compare the performance of the penalization approaches.

lassopack: Model selection and prediction with regularized regression in Stata

2019-01-16 20:30:27

Achim Ahrens, Christian B. Hansen, Mark E. Schaffer

http://arxiv.org/abs/1901.05397v1, http://arxiv.org/pdf/1901.05397v1

econ.EM

em

The maximum utility estimation proposed by Elliott and Lieli (2013) can be viewed as cost-sensitive binary classification; thus, its in-sample overfitting issue is similar to that of perceptron learning. A utility-maximizing prediction rule (UMPR) is constructed to alleviate the in-sample overfitting of the maximum utility estimation. We establish non-asymptotic upper bounds on the difference between the maximal expected utility and the generalized expected utility of the UMPR. Simulation results show that the UMPR with an appropriate data-dependent penalty achieves larger generalized expected utility than common estimators in the binary classification if the conditional probability of the binary outcome is misspecified.

Model Selection in Utility-Maximizing Binary Prediction

2019-03-02 18:02:50

Jiun-Hua Su

http://dx.doi.org/10.1016/j.jeconom.2020.07.052, http://arxiv.org/abs/1903.00716v3, http://arxiv.org/pdf/1903.00716v3

econ.EM

em

We provide a finite sample inference method for the structural parameters of a semiparametric binary response model under a conditional median restriction originally studied by Manski (1975, 1985). Our inference method is valid for any sample size and irrespective of whether the structural parameters are point identified or partially identified, for example due to the lack of a continuously distributed covariate with large support. Our inference approach exploits distributional properties of observable outcomes conditional on the observed sequence of exogenous variables. Moment inequalities conditional on this size n sequence of exogenous covariates are constructed, and the test statistic is a monotone function of violations of sample moment inequalities. The critical value used for inference is provided by the appropriate quantile of a known function of n independent Rademacher random variables. We investigate power properties of the underlying test and provide simulation studies to support the theoretical findings.

Finite Sample Inference for the Maximum Score Estimand

2019-03-04 22:53:00

Adam M. Rosen, Takuya Ura

http://arxiv.org/abs/1903.01511v2, http://arxiv.org/pdf/1903.01511v2

econ.EM

em

A fundamental problem with nonlinear models is that maximum likelihood estimates are not guaranteed to exist. Though nonexistence is a well known problem in the binary choice literature, it presents significant challenges for other models as well and is not as well understood in more general settings. These challenges are only magnified for models that feature many fixed effects and other high-dimensional parameters. We address the current ambiguity surrounding this topic by studying the conditions that govern the existence of estimates for (pseudo-)maximum likelihood estimators used to estimate a wide class of generalized linear models (GLMs). We show that some, but not all, of these GLM estimators can still deliver consistent estimates of at least some of the linear parameters when these conditions fail to hold. We also demonstrate how to verify these conditions in models with high-dimensional parameters, such as panel data models with multiple levels of fixed effects.

Verifying the existence of maximum likelihood estimates for generalized linear models

2019-03-05 05:18:49

Sergio Correia, Paulo Guimarães, Thomas Zylkin

http://arxiv.org/abs/1903.01633v6, http://arxiv.org/pdf/1903.01633v6

econ.EM

em

Bojinov & Shephard (2019) defined potential outcome time series to nonparametrically measure dynamic causal effects in time series experiments. Four innovations are developed in this paper: "instrumental paths," treatments which are "shocks," "linear potential outcomes" and the "causal response function." Potential outcome time series are then used to provide a nonparametric causal interpretation of impulse response functions, generalized impulse response functions, local projections and LP-IV.

Econometric analysis of potential outcomes time series: instruments, shocks, linearity and the causal response function

2019-03-05 05:53:08

Ashesh Rambachan, Neil Shephard

http://arxiv.org/abs/1903.01637v3, http://arxiv.org/pdf/1903.01637v3

econ.EM

em

In this paper we present ppmlhdfe, a new Stata command for estimation of (pseudo) Poisson regression models with multiple high-dimensional fixed effects (HDFE). Estimation is implemented using a modified version of the iteratively reweighted least-squares (IRLS) algorithm that allows for fast estimation in the presence of HDFE. Because the code is built around the reghdfe package, it has similar syntax, supports many of the same functionalities, and benefits from reghdfe's fast convergence properties for computing high-dimensional least squares problems. Performance is further enhanced by some new techniques we introduce for accelerating HDFE-IRLS estimation specifically. ppmlhdfe also implements a novel and more robust approach to check for the existence of (pseudo) maximum likelihood estimates.

ppmlhdfe: Fast Poisson Estimation with High-Dimensional Fixed Effects

2019-03-05 09:11:26

Sergio Correia, Paulo Guimarães, Thomas Zylkin

http://dx.doi.org/10.1177/1536867X20909691, http://arxiv.org/abs/1903.01690v3, http://arxiv.org/pdf/1903.01690v3

econ.EM

em

A fixed effects regression estimator is introduced that can directly identify and estimate the Africa-Dummy in one regression step so that its correct standard errors as well as correlations to other coefficients can easily be estimated. We can estimate the Nickel bias and found it to be negligibly tiny. Semiparametric extensions check whether the Africa-Dummy is simply a result of misspecification of the functional form. In particular, we show that the returns to growth factors are different for Sub-Saharan African countries compared to the rest of the world. For example, returns to population growth are positive and beta-convergence is faster. When extending the model to identify the development of the Africa-Dummy over time we see that it has been changing dramatically over time and that the punishment for Sub-Saharan African countries has been decreasing incrementally to reach insignificance around the turn of the millennium.

The Africa-Dummy: Gone with the Millennium?

2019-03-06 16:18:13

Max Köhler, Stefan Sperlich

http://arxiv.org/abs/1903.02357v1, http://arxiv.org/pdf/1903.02357v1

econ.EM

em

Various papers demonstrate the importance of inequality, poverty and the size of the middle class for economic growth. When explaining why these measures of the income distribution are added to the growth regression, it is often mentioned that poor people behave different which may translate to the economy as a whole. However, simply adding explanatory variables does not reflect this behavior. By a varying coefficient model we show that the returns to growth differ a lot depending on poverty and inequality. Furthermore, we investigate how these returns differ for the poorer and for the richer part of the societies. We argue that the differences in the coefficients impede, on the one hand, that the means coefficients are informative, and, on the other hand, challenge the credibility of the economic interpretation. In short, we show that, when estimating mean coefficients without accounting for poverty and inequality, the estimation is likely to suffer from a serious endogeneity bias.

A Varying Coefficient Model for Assessing the Returns to Growth to Account for Poverty and Inequality

2019-03-06 17:07:05

Max Köhler, Stefan Sperlich, Jisu Yoon

http://arxiv.org/abs/1903.02390v1, http://arxiv.org/pdf/1903.02390v1

econ.EM

em

We consider inference on the probability density of valuations in the first-price sealed-bid auctions model within the independent private value paradigm. We show the asymptotic normality of the two-step nonparametric estimator of Guerre, Perrigne, and Vuong (2000) (GPV), and propose an easily implementable and consistent estimator of the asymptotic variance. We prove the validity of the pointwise percentile bootstrap confidence intervals based on the GPV estimator. Lastly, we use the intermediate Gaussian approximation approach to construct bootstrap-based asymptotically valid uniform confidence bands for the density of the valuations.

Inference for First-Price Auctions with Guerre, Perrigne, and Vuong's Estimator

2019-03-15 11:09:33

Jun Ma, Vadim Marmer, Artyom Shneyerov

http://dx.doi.org/10.1016/j.jeconom.2019.02.006, http://arxiv.org/abs/1903.06401v1, http://arxiv.org/pdf/1903.06401v1

econ.EM

em

Empirical growth analysis has three major problems --- variable selection, parameter heterogeneity and cross-sectional dependence --- which are addressed independently from each other in most studies. The purpose of this study is to propose an integrated framework that extends the conventional linear growth regression model to allow for parameter heterogeneity and cross-sectional error dependence, while simultaneously performing variable selection. We also derive the asymptotic properties of the estimator under both low and high dimensions, and further investigate the finite sample performance of the estimator through Monte Carlo simulations. We apply the framework to a dataset of 89 countries over the period from 1960 to 2014. Our results reveal some cross-country patterns not found in previous studies (e.g., "middle income trap hypothesis", "natural resources curse hypothesis", "religion works via belief, not practice", etc.).

An Integrated Panel Data Approach to Modelling Economic Growth

2019-03-19 14:38:09

Guohua Feng, Jiti Gao, Bin Peng

http://arxiv.org/abs/1903.07948v1, http://arxiv.org/pdf/1903.07948v1

econ.EM

em

We propose a new approach to mixed-frequency regressions in a high-dimensional environment that resorts to Group Lasso penalization and Bayesian techniques for estimation and inference. In particular, to improve the prediction properties of the model and its sparse recovery ability, we consider a Group Lasso with a spike-and-slab prior. Penalty hyper-parameters governing the model shrinkage are automatically tuned via an adaptive MCMC algorithm. We establish good frequentist asymptotic properties of the posterior of the in-sample and out-of-sample prediction error, we recover the optimal posterior contraction rate, and we show optimality of the posterior predictive density. Simulations show that the proposed models have good selection and forecasting performance in small samples, even when the design matrix presents cross-correlation. When applied to forecasting U.S. GDP, our penalized regressions can outperform many strong competitors. Results suggest that financial variables may have some, although very limited, short-term predictive content.

Bayesian MIDAS Penalized Regressions: Estimation, Selection, and Prediction

2019-03-19 17:42:37

Matteo Mogliani, Anna Simoni

http://arxiv.org/abs/1903.08025v3, http://arxiv.org/pdf/1903.08025v3

econ.EM

em

I study a regression model in which one covariate is an unknown function of a latent driver of link formation in a network. Rather than specify and fit a parametric network formation model, I introduce a new method based on matching pairs of agents with similar columns of the squared adjacency matrix, the ijth entry of which contains the number of other agents linked to both agents i and j. The intuition behind this approach is that for a large class of network formation models the columns of the squared adjacency matrix characterize all of the identifiable information about individual linking behavior. In this paper, I describe the model, formalize this intuition, and provide consistent estimators for the parameters of the regression model. Auerbach (2021) considers inference and an application to network peer effects.

Identification and Estimation of a Partially Linear Regression Model using Network Data

2019-03-22 21:59:22

Eric Auerbach

http://arxiv.org/abs/1903.09679v3, http://arxiv.org/pdf/1903.09679v3

econ.EM

em

This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.

Ensemble Methods for Causal Effects in Panel Data Settings

2019-03-25 02:21:52

Susan Athey, Mohsen Bayati, Guido Imbens, Zhaonan Qu

http://arxiv.org/abs/1903.10079v1, http://arxiv.org/pdf/1903.10079v1

econ.EM

em

How can one determine whether a community-level treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogues of a two-sample Kolmogorov-Smirnov test, widely used in the literature to test the null hypothesis of "no treatment effects", for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the $2\to2$ and $\infty\to1$ operator norms. Power properties of the tests are examined analytically, in simulation, and through two real-world applications. A key finding is that the test based on the $\infty\to1$ norm can be substantially more powerful than that based on the $2\to2$ norm for the kinds of sparse and degree-heterogeneous networks common in economics.

Testing for Differences in Stochastic Network Structure

2019-03-26 22:00:45

Eric Auerbach

http://arxiv.org/abs/1903.11117v5, http://arxiv.org/pdf/1903.11117v5

econ.EM

em

This paper studies a regularized support function estimator for bounds on components of the parameter vector in the case in which the identified set is a polygon. The proposed regularized estimator has three important properties: (i) it has a uniform asymptotic Gaussian limit in the presence of flat faces in the absence of redundant (or overidentifying) constraints (or vice versa); (ii) the bias from regularization does not enter the first-order limiting distribution;(iii) the estimator remains consistent for sharp identified set for the individual components even in the non-regualar case. These properties are used to construct uniformly valid confidence sets for an element $\theta_{1}$ of a parameter vector $\theta\in\mathbb{R}^{d}$ that is partially identified by affine moment equality and inequality conditions. The proposed confidence sets can be computed as a solution to a small number of linear and convex quadratic programs, which leads to a substantial decrease in computation time and guarantees a global optimum. As a result, the method provides uniformly valid inference in applications in which the dimension of the parameter space, $d$, and the number of inequalities, $k$, were previously computationally unfeasible ($d,k=100$). The proposed approach can be extended to construct confidence sets for intersection bounds, to construct joint polygon-shaped confidence sets for multiple components of $\theta$, and to find the set of solutions to a linear program. Inference for coefficients in the linear IV regression model with an interval outcome is used as an illustrative example.

Simple subvector inference on sharp identified set in affine models

2019-03-30 01:49:40

Bulat Gafarov

http://arxiv.org/abs/1904.00111v2, http://arxiv.org/pdf/1904.00111v2

econ.EM

em

Three-dimensional panel models are widely used in empirical analysis. Researchers use various combinations of fixed effects for three-dimensional panels. When one imposes a parsimonious model and the true model is rich, then it incurs mis-specification biases. When one employs a rich model and the true model is parsimonious, then it incurs larger standard errors than necessary. It is therefore useful for researchers to know correct models. In this light, Lu, Miao, and Su (2018) propose methods of model selection. We advance this literature by proposing a method of post-selection inference for regression parameters. Despite our use of the lasso technique as means of model selection, our assumptions allow for many and even all fixed effects to be nonzero. Simulation studies demonstrate that the proposed method is more precise than under-fitting fixed effect estimators, is more efficient than over-fitting fixed effect estimators, and allows for as accurate inference as the oracle estimator.

Post-Selection Inference in Three-Dimensional Panel Data

2019-03-30 15:51:35

Harold D. Chiang, Joel Rodrigue, Yuya Sasaki

http://arxiv.org/abs/1904.00211v2, http://arxiv.org/pdf/1904.00211v2

econ.EM

em

We propose a framework for analyzing the sensitivity of counterfactuals to parametric assumptions about the distribution of latent variables in structural models. In particular, we derive bounds on counterfactuals as the distribution of latent variables spans nonparametric neighborhoods of a given parametric specification while other "structural" features of the model are maintained. Our approach recasts the infinite-dimensional problem of optimizing the counterfactual with respect to the distribution of latent variables (subject to model constraints) as a finite-dimensional convex program. We also develop an MPEC version of our method to further simplify computation in models with endogenous parameters (e.g., value functions) defined by equilibrium constraints. We propose plug-in estimators of the bounds and two methods for inference. We also show that our bounds converge to the sharp nonparametric bounds on counterfactuals as the neighborhood size becomes large. To illustrate the broad applicability of our procedure, we present empirical applications to matching models with transferable utility and dynamic discrete choice models.

Counterfactual Sensitivity and Robustness

2019-04-01 20:53:20

Timothy Christensen, Benjamin Connault

http://arxiv.org/abs/1904.00989v4, http://arxiv.org/pdf/1904.00989v4

econ.EM

em

Models with a discrete endogenous variable are typically underidentified when the instrument takes on too few values. This paper presents a new method that matches pairs of covariates and instruments to restore point identification in this scenario in a triangular model. The model consists of a structural function for a continuous outcome and a selection model for the discrete endogenous variable. The structural outcome function must be continuous and monotonic in a scalar disturbance, but it can be nonseparable. The selection model allows for unrestricted heterogeneity. Global identification is obtained under weak conditions. The paper also provides estimators of the structural outcome function. Two empirical examples of the return to education and selection into Head Start illustrate the value and limitations of the method.

Matching Points: Supplementing Instruments with Covariates in Triangular Models

2019-04-02 04:12:10

Junlong Feng

http://arxiv.org/abs/1904.01159v3, http://arxiv.org/pdf/1904.01159v3

econ.EM

em

Empirical economists are often deterred from the application of fixed effects binary choice models mainly for two reasons: the incidental parameter problem and the computational challenge even in moderately large panels. Using the example of binary choice models with individual and time fixed effects, we show how both issues can be alleviated by combining asymptotic bias corrections with computational advances. Because unbalancedness is often encountered in applied work, we investigate its consequences on the finite sample properties of various (bias corrected) estimators. In simulation experiments we find that analytical bias corrections perform particularly well, whereas split-panel jackknife estimators can be severely biased in unbalanced panels.

Fixed Effects Binary Choice Models: Estimation and Inference with Long Panels

2019-04-08 20:38:31

Daniel Czarnowske, Amrei Stammann

http://arxiv.org/abs/1904.04217v3, http://arxiv.org/pdf/1904.04217v3

econ.EM

em

This article proposes inference procedures for distribution regression models in duration analysis using randomly right-censored data. This generalizes classical duration models by allowing situations where explanatory variables' marginal effects freely vary with duration time. The article discusses applications to testing uniform restrictions on the varying coefficients, inferences on average marginal effects, and others involving conditional distribution estimates. Finite sample properties of the proposed method are studied by means of Monte Carlo experiments. Finally, we apply our proposal to study the effects of unemployment benefits on unemployment duration.

Distribution Regression in Duration Analysis: an Application to Unemployment Spells

2019-04-12 15:22:27

Miguel A. Delgado, Andrés García-Suaza, Pedro H. C. Sant'Anna

http://arxiv.org/abs/1904.06185v2, http://arxiv.org/pdf/1904.06185v2

econ.EM

em

Internet finance is a new financial model that applies Internet technology to payment, capital borrowing and lending and transaction processing. In order to study the internal risks, this paper uses the Internet financial risk elements as the network node to construct the complex network of Internet financial risk system. Different from the study of macroeconomic shocks and financial institution data, this paper mainly adopts the perspective of complex system to analyze the systematic risk of Internet finance. By dividing the entire financial system into Internet financial subnet, regulatory subnet and traditional financial subnet, the paper discusses the relationship between contagion and contagion among different risk factors, and concludes that risks are transmitted externally through the internal circulation of Internet finance, thus discovering potential hidden dangers of systemic risks. The results show that the nodes around the center of the whole system are the main objects of financial risk contagion in the Internet financial network. In addition, macro-prudential regulation plays a decisive role in the control of the Internet financial system, and points out the reasons why the current regulatory measures are still limited. This paper summarizes a research model which is still in its infancy, hoping to open up new prospects and directions for us to understand the cascading behaviors of Internet financial risks.

Complex Network Construction of Internet Financial risk

2019-04-14 09:55:11

Runjie Xu, Chuanmin Mi, Rafal Mierzwiak, Runyu Meng

http://dx.doi.org/10.1016/j.physa.2019.122930, http://arxiv.org/abs/1904.06640v3, http://arxiv.org/pdf/1904.06640v3

econ.EM

em

We develop a dynamic model of discrete choice that incorporates peer effects into random consideration sets. We characterize the equilibrium behavior and study the empirical content of the model. In our setup, changes in the choices of friends affect the distribution of the consideration sets. We exploit this variation to recover the ranking of preferences, attention mechanisms, and network connections. These nonparametric identification results allow unrestricted heterogeneity across people and do not rely on the variation of either covariates or the set of available options. Our methodology leads to a maximum-likelihood estimator that performs well in simulations. We apply our results to an experimental dataset that has been designed to study the visual focus of attention.

Peer Effects in Random Consideration Sets

2019-04-14 22:15:07

Nail Kashaev, Natalia Lazzati

http://arxiv.org/abs/1904.06742v3, http://arxiv.org/pdf/1904.06742v3

econ.EM

em

In multinomial response models, idiosyncratic variations in the indirect utility are generally modeled using Gumbel or normal distributions. This study makes a strong case to substitute these thin-tailed distributions with a t-distribution. First, we demonstrate that a model with a t-distributed error kernel better estimates and predicts preferences, especially in class-imbalanced datasets. Our proposed specification also implicitly accounts for decision-uncertainty behavior, i.e. the degree of certainty that decision-makers hold in their choices relative to the variation in the indirect utility of any alternative. Second, after applying a t-distributed error kernel in a multinomial response model for the first time, we extend this specification to a generalized continuous-multinomial (GCM) model and derive its full-information maximum likelihood estimator. The likelihood involves an open-form expression of the cumulative density function of the multivariate t-distribution, which we propose to compute using a combination of the composite marginal likelihood method and the separation-of-variables approach. Third, we establish finite sample properties of the GCM model with a t-distributed error kernel (GCM-t) and highlight its superiority over the GCM model with a normally-distributed error kernel (GCM-N) in a Monte Carlo study. Finally, we compare GCM-t and GCM-N in an empirical setting related to preferences for electric vehicles (EVs). We observe that accounting for decision-uncertainty behavior in GCM-t results in lower elasticity estimates and a higher willingness to pay for improving the EV attributes than those of the GCM-N model. These differences are relevant in making policies to expedite the adoption of EVs.

A Generalized Continuous-Multinomial Response Model with a t-distributed Error Kernel

2019-04-17 18:54:04

Subodh Dubey, Prateek Bansal, Ricardo A. Daziano, Erick Guerra

http://arxiv.org/abs/1904.08332v3, http://arxiv.org/pdf/1904.08332v3

econ.EM

em

Currently all countries including developing countries are expected to utilize their own tax revenues and carry out their own development for solving poverty in their countries. However, developing countries cannot earn tax revenues like developed countries partly because they do not have effective countermeasures against international tax avoidance. Our analysis focuses on treaty shopping among various ways to conduct international tax avoidance because tax revenues of developing countries have been heavily damaged through treaty shopping. To analyze the location and sector of conduit firms likely to be used for treaty shopping, we constructed a multilayer ownership-tax network and proposed multilayer centrality. Because multilayer centrality can consider not only the value owing in the ownership network but also the withholding tax rate, it is expected to grasp precisely the locations and sectors of conduit firms established for the purpose of treaty shopping. Our analysis shows that firms in the sectors of Finance & Insurance and Wholesale & Retail trade etc. are involved with treaty shopping. We suggest that developing countries make a clause focusing on these sectors in the tax treaties they conclude.

Location-Sector Analysis of International Profit Shifting on a Multilayer Ownership-Tax Network

2019-04-19 15:30:34

Tembo Nakamoto, Odile Rouhban, Yuichi Ikeda

http://arxiv.org/abs/1904.09165v1, http://arxiv.org/pdf/1904.09165v1

econ.EM

em

This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary least squares (OLS) in the presence of heteroskedasticity, serial, and cross-sectional correlations. To take into account the serial correlations, we employ the banding method. To take into account the cross-sectional correlations, we suggest to use the thresholding method. We establish the limiting distribution of the proposed estimator. A Monte Carlo study is considered. The proposed method is applied to an empirical application.

Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations

2019-10-20 18:37:51

Jushan Bai, Sung Hoon Choi, Yuan Liao

http://arxiv.org/abs/1910.09004v3, http://arxiv.org/pdf/1910.09004v3

econ.EM

em

We study identification in nonparametric regression models with a misclassified and endogenous binary regressor when an instrument is correlated with misclassification error. We show that the regression function is nonparametrically identified if one binary instrument variable and one binary covariate satisfy the following conditions. The instrumental variable corrects endogeneity; the instrumental variable must be correlated with the unobserved true underlying binary variable, must be uncorrelated with the error term in the outcome equation, but is allowed to be correlated with the misclassification error. The covariate corrects misclassification; this variable can be one of the regressors in the outcome equation, must be correlated with the unobserved true underlying binary variable, and must be uncorrelated with the misclassification error. We also propose a mixture-based framework for modeling unobserved heterogeneous treatment effects with a misclassified and endogenous binary regressor and show that treatment effects can be identified if the true treatment effect is related to an observed regressor and another observable variable.

Identification of Regression Models with a Misclassified and Endogenous Binary Regressor

2019-04-25 06:41:37

Hiroyuki Kasahara, Katsumi Shimotsu

http://arxiv.org/abs/1904.11143v3, http://arxiv.org/pdf/1904.11143v3

econ.EM

em

In matched-pairs experiments in which one cluster per pair of clusters is assigned to treatment, to estimate treatment effects, researchers often regress their outcome on a treatment indicator and pair fixed effects, clustering standard errors at the unit-ofrandomization level. We show that even if the treatment has no effect, a 5%-level t-test based on this regression will wrongly conclude that the treatment has an effect up to 16.5% of the time. To fix this problem, researchers should instead cluster standard errors at the pair level. Using simulations, we show that similar results apply to clustered experiments with small strata.

At What Level Should One Cluster Standard Errors in Paired and Small-Strata Experiments?

2019-06-01 23:47:18

Clément de Chaisemartin, Jaime Ramirez-Cuellar

http://arxiv.org/abs/1906.00288v10, http://arxiv.org/pdf/1906.00288v10

econ.EM

em

We propose the use of indirect inference estimation to conduct inference in complex locally stationary models. We develop a local indirect inference algorithm and establish the asymptotic properties of the proposed estimator. Due to the nonparametric nature of locally stationary models, the resulting indirect inference estimator exhibits nonparametric rates of convergence. We validate our methodology with simulation studies in the confines of a locally stationary moving average model and a new locally stationary multiplicative stochastic volatility model. Using this indirect inference methodology and the new locally stationary volatility model, we obtain evidence of non-linear, time-varying volatility trends for monthly returns on several Fama-French portfolios.

Indirect Inference for Locally Stationary Models

2019-06-05 03:41:13

David Frazier, Bonsoo Koo

http://dx.doi.org/10.1016/S0304-4076/20/30303-1, http://arxiv.org/abs/1906.01768v2, http://arxiv.org/pdf/1906.01768v2

econ.EM

em

In a nonparametric instrumental regression model, we strengthen the conventional moment independence assumption towards full statistical independence between instrument and error term. This allows us to prove identification results and develop estimators for a structural function of interest when the instrument is discrete, and in particular binary. When the regressor of interest is also discrete with more mass points than the instrument, we state straightforward conditions under which the structural function is partially identified, and give modified assumptions which imply point identification. These stronger assumptions are shown to hold outside of a small set of conditional moments of the error term. Estimators for the identified set are given when the structural function is either partially or point identified. When the regressor is continuously distributed, we prove that if the instrument induces a sufficiently rich variation in the joint distribution of the regressor and error term then point identification of the structural function is still possible. This approach is relatively tractable, and under some standard conditions we demonstrate that our point identifying assumption holds on a topologically generic set of density functions for the joint distribution of regressor, error, and instrument. Our method also applies to a well-known nonparametric quantile regression framework, and we are able to state analogous point identification results in that context.

Nonparametric Identification and Estimation with Independent, Discrete Instruments

2019-06-12 19:05:52

Isaac Loh

http://arxiv.org/abs/1906.05231v1, http://arxiv.org/pdf/1906.05231v1

econ.EM

em

We consider the asymptotic properties of the Synthetic Control (SC) estimator when both the number of pre-treatment periods and control units are large. If potential outcomes follow a linear factor model, we provide conditions under which the factor loadings of the SC unit converge in probability to the factor loadings of the treated unit. This happens when there are weights diluted among an increasing number of control units such that a weighted average of the factor loadings of the control units asymptotically reconstructs the factor loadings of the treated unit. In this case, the SC estimator is asymptotically unbiased even when treatment assignment is correlated with time-varying unobservables. This result can be valid even when the number of control units is larger than the number of pre-treatment periods.

On the Properties of the Synthetic Control Estimator with Many Periods and Many Controls

2019-06-16 15:26:28

Bruno Ferman

http://arxiv.org/abs/1906.06665v5, http://arxiv.org/pdf/1906.06665v5

econ.EM

em

We study the association between physical appearance and family income using a novel data which has 3-dimensional body scans to mitigate the issue of reporting errors and measurement errors observed in most previous studies. We apply machine learning to obtain intrinsic features consisting of human body and take into account a possible issue of endogenous body shapes. The estimation results show that there is a significant relationship between physical appearance and family income and the associations are different across the gender. This supports the hypothesis on the physical attractiveness premium and its heterogeneity across the gender.

Shape Matters: Evidence from Machine Learning on Body Shape-Income Relationship

2019-06-16 21:42:22

Suyong Song, Stephen S. Baek

http://dx.doi.org/10.1371/journal.pone.0254785, http://arxiv.org/abs/1906.06747v1, http://arxiv.org/pdf/1906.06747v1

econ.EM

em

This paper considers estimation of large dynamic factor models with common and idiosyncratic trends by means of the Expectation Maximization algorithm, implemented jointly with the Kalman smoother. We show that, as the cross-sectional dimension $n$ and the sample size $T$ diverge to infinity, the common component for a given unit estimated at a given point in time is $\min(\sqrt n,\sqrt T)$-consistent. The case of local levels and/or local linear trends trends is also considered. By means of a MonteCarlo simulation exercise, we compare our approach with estimators based on principal component analysis.

Quasi Maximum Likelihood Estimation of Non-Stationary Large Approximate Dynamic Factor Models

2019-10-22 12:00:06

Matteo Barigozzi, Matteo Luciani

http://arxiv.org/abs/1910.09841v1, http://arxiv.org/pdf/1910.09841v1

econ.EM

em

This paper aims to examine the use of sparse methods to forecast the real, in the chain-linked volume sense, expenditure components of the US and EU GDP in the short-run sooner than the national institutions of statistics officially release the data. We estimate current quarter nowcasts along with 1- and 2-quarter forecasts by bridging quarterly data with available monthly information announced with a much smaller delay. We solve the high-dimensionality problem of the monthly dataset by assuming sparse structures of leading indicators, capable of adequately explaining the dynamics of analyzed data. For variable selection and estimation of the forecasts, we use the sparse methods - LASSO together with its recent modifications. We propose an adjustment that combines LASSO cases with principal components analysis that deemed to improve the forecasting performance. We evaluate forecasting performance conducting pseudo-real-time experiments for gross fixed capital formation, private consumption, imports and exports over the sample of 2005-2019, compared with benchmark ARMA and factor models. The main results suggest that sparse methods can outperform the benchmarks and to identify reasonable subsets of explanatory variables. The proposed LASSO-PC modification show further improvement in forecast accuracy.

Sparse structures with LASSO through Principal Components: forecasting GDP components in the short-run

2019-06-19 12:30:36

Saulius Jokubaitis, Dmitrij Celov, Remigijus Leipus

http://dx.doi.org/10.1016/j.ijforecast.2020.09.005, http://arxiv.org/abs/1906.07992v2, http://arxiv.org/pdf/1906.07992v2

econ.EM

em

In 2018, allowance prices in the EU Emission Trading Scheme (EU ETS) experienced a run-up from persistently low levels in previous years. Regulators attribute this to a comprehensive reform in the same year, and are confident the new price level reflects an anticipated tighter supply of allowances. We ask if this is indeed the case, or if it is an overreaction of the market driven by speculation. We combine several econometric methods - time-varying coefficient regression, formal bubble detection as well as time stamping and crash odds prediction - to juxtapose the regulators' claim versus the concurrent explanation. We find evidence of a long period of explosive behaviour in allowance prices, starting in March 2018 when the reform was adopted. Our results suggest that the reform triggered market participants into speculation, and question regulators' confidence in its long-term outcome. This has implications for both the further development of the EU ETS, and the long lasting debate about taxes versus emission trading schemes.

Understanding the explosive trend in EU ETS prices -- fundamentals or speculation?

2019-06-25 17:43:50

Marina Friedrich, Sébastien Fries, Michael Pahle, Ottmar Edenhofer

http://arxiv.org/abs/1906.10572v5, http://arxiv.org/pdf/1906.10572v5

econ.EM

em

Many economic studies use shift-share instruments to estimate causal effects. Often, all shares need to fulfil an exclusion restriction, making the identifying assumption strict. This paper proposes to use methods that relax the exclusion restriction by selecting invalid shares. I apply the methods in two empirical examples: the effect of immigration on wages and of Chinese import exposure on employment. In the first application, the coefficient becomes lower and often changes sign, but this is reconcilable with arguments made in the literature. In the second application, the findings are mostly robust to the use of the new methods.

Relaxing the Exclusion Restriction in Shift-Share Instrumental Variable Estimation

2019-06-29 18:27:49

Nicolas Apfel

http://arxiv.org/abs/1907.00222v4, http://arxiv.org/pdf/1907.00222v4

econ.EM

em

There is currently an increasing interest in large vector autoregressive (VAR) models. VARs are popular tools for macroeconomic forecasting and use of larger models has been demonstrated to often improve the forecasting ability compared to more traditional small-scale models. Mixed-frequency VARs deal with data sampled at different frequencies while remaining within the realms of VARs. Estimation of mixed-frequency VARs makes use of simulation smoothing, but using the standard procedure these models quickly become prohibitive in nowcasting situations as the size of the model grows. We propose two algorithms that alleviate the computational efficiency of the simulation smoothing algorithm. Our preferred choice is an adaptive algorithm, which augments the state vector as necessary to sample also monthly variables that are missing at the end of the sample. For large VARs, we find considerable improvements in speed using our adaptive algorithm. The algorithm therefore provides a crucial building block for bringing the mixed-frequency VARs to the high-dimensional regime.

Simulation smoothing for nowcasting with large mixed-frequency VARs

2019-07-02 00:08:21

Sebastian Ankargren, Paulina Jonéus

http://arxiv.org/abs/1907.01075v1, http://arxiv.org/pdf/1907.01075v1

econ.EM

em

We propose a robust method of discrete choice analysis when agents' choice sets are unobserved. Our core model assumes nothing about agents' choice sets apart from their minimum size. Importantly, it leaves unrestricted the dependence, conditional on observables, between choice sets and preferences. We first characterize the sharp identification region of the model's parameters by a finite set of conditional moment inequalities. We then apply our theoretical findings to learn about households' risk preferences and choice sets from data on their deductible choices in auto collision insurance. We find that the data can be explained by expected utility theory with low levels of risk aversion and heterogeneous non-singleton choice sets, and that more than three in four households require limited choice sets to explain their deductible choices. We also provide simulation evidence on the computational tractability of our method in applications with larger feasible sets or higher-dimensional unobserved heterogeneity.

Heterogeneous Choice Sets and Preferences

2019-07-04 14:47:26

Levon Barseghyan, Maura Coughlin, Francesca Molinari, Joshua C. Teitelbaum

http://arxiv.org/abs/1907.02337v2, http://arxiv.org/pdf/1907.02337v2

econ.EM

em

In this paper we develop a new machine learning estimator for ordered choice models based on the random forest. The proposed Ordered Forest flexibly estimates the conditional choice probabilities while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference and thus provides the same output as classical econometric estimators. An extensive simulation study reveals a good predictive performance, particularly in settings with non-linearities and near-multicollinearity. An empirical application contrasts the estimation of marginal effects and their standard errors with an ordered logit model. A software implementation of the Ordered Forest is provided both in R and Python in the package orf available on CRAN and PyPI, respectively.

Random Forest Estimation of the Ordered Choice Model

2019-07-04 17:54:58

Michael Lechner, Gabriel Okasa

http://arxiv.org/abs/1907.02436v3, http://arxiv.org/pdf/1907.02436v3

econ.EM

em

This paper provides tests for detecting sample selection in nonparametric conditional quantile functions. The first test is an omitted predictor test with the propensity score as the omitted variable. As with any omnibus test, in the case of rejection we cannot distinguish between rejection due to genuine selection or to misspecification. Thus, we suggest a second test to provide supporting evidence whether the cause for rejection at the first stage was solely due to selection or not. Using only individuals with propensity score close to one, this second test relies on an `identification at infinity' argument, but accommodates cases of irregular identification. Importantly, neither of the two tests requires parametric assumptions on the selection equation nor a continuous exclusion restriction. Data-driven bandwidth procedures are proposed, and Monte Carlo evidence suggests a good finite sample performance in particular of the first test. Finally, we also derive an extension of the first test to nonparametric conditional mean functions, and apply our procedure to test for selection in log hourly wages using UK Family Expenditure Survey data as \citet{AB2017}.

Testing for Quantile Sample Selection

2019-07-17 12:39:39

Valentina Corradi, Daniel Gutknecht

http://arxiv.org/abs/1907.07412v5, http://arxiv.org/pdf/1907.07412v5

econ.EM

em

Clustering methods such as k-means have found widespread use in a variety of applications. This paper proposes a formal testing procedure to determine whether a null hypothesis of a single cluster, indicating homogeneity of the data, can be rejected in favor of multiple clusters. The test is simple to implement, valid under relatively mild conditions (including non-normality, and heterogeneity of the data in aspects beyond those in the clustering analysis), and applicable in a range of contexts (including clustering when the time series dimension is small, or clustering on parameters other than the mean). We verify that the test has good size control in finite samples, and we illustrate the test in applications to clustering vehicle manufacturers and U.S. mutual funds.

Testing for Unobserved Heterogeneity via k-means Clustering

2019-07-17 18:28:24

Andrew J. Patton, Brian M. Weller

http://arxiv.org/abs/1907.07582v1, http://arxiv.org/pdf/1907.07582v1

econ.EM