pwc-archive/papers-with-abstracts huggingface dataset:
Interpreting TSLS Estimators in Information Provision Experiments
Authors: Vod Vilfort, Whitney Zhang
Date: 2023-09-09 00:00:00

To estimate the causal effects of beliefs on actions, researchers often run information provision experiments. We consider the causal interpretation of two-stage least squares (TSLS) estimators in these experiments. We characterize common TSLS estimators as weighted averages of causal effects, and interpret these weights under general belief updating conditions that nest parametric models from the literature. Our framework accommodates TSLS estimators for both passive and active control designs. Notably, we find that some passive control estimators allow for negative weights, which compromises their causal interpretation. We give practical guidance on such issues, and illustrate our results in two empirical applications.
Identifying spatial interdependence in panel data with large N and small T
Authors: Deborah Gefang, Stephen G. Hall, George S. Tavlas
Date: 2023-09-07 00:00:00
Tasks: Variable Selection

This paper develops a simple two-stage variational Bayesian algorithm to estimate panel spatial autoregressive models, where N, the number of cross-sectional units, is much larger than T, the number of time periods without restricting the spatial effects using a predetermined weighting matrix. We use Dirichlet-Laplace priors for variable selection and parameter shrinkage. Without imposing any a priori structures on the spatial linkages between variables, we let the data speak for themselves. Extensive Monte Carlo studies show that our method is super-fast and our estimated spatial weights matrices strongly resemble the true spatial weights matrices. As an illustration, we investigate the spatial interdependence of European Union regional gross value added growth rates. In addition to a clear pattern of predominant country clusters, we have uncovered a number of important between-country spatial linkages which are yet to be documented in the literature. This new procedure for estimating spatial effects is of particular relevance for researchers and policy makers alike.
Instrumental variable estimation of the proportional hazards model by presmoothing
Authors: Lorenzo Tedesco, Jad Beyhum, Ingrid Van Keilegom
Date: 2023-09-05 00:00:00
Tasks: quantile regression, regression

We consider instrumental variable estimation of the proportional hazards model of Cox (1972). The instrument and the endogenous variable are discrete but there can be (possibly continuous) exogenous covariables. By making a rank invariance assumption, we can reformulate the proportional hazards model into a semiparametric version of the instrumental variable quantile regression model of Chernozhukov and Hansen (2005). A na\"ive estimation approach based on conditional moment conditions generated by the model would lead to a highly nonconvex and nonsmooth objective function. To overcome this problem, we propose a new presmoothing methodology. First, we estimate the model nonparametrically - and show that this nonparametric estimator has a closed-form solution in the leading case of interest of randomized experiments with one-sided noncompliance. Second, we use the nonparametric estimator to generate ``proxy'' observations for which exogeneity holds. Third, we apply the usual partial likelihood estimator to the ``proxy'' data. While the paper focuses on the proportional hazards model, our presmoothing approach could be applied to estimate other semiparametric formulations of the instrumental variable quantile regression model. Our estimation procedure allows for random right-censoring. We show asymptotic normality of the resulting estimator. The approach is illustrated via simulation studies and an empirical application to the Illinois
On the use of U-statistics for linear dyadic interaction models
Authors: G. M. Szini
Date: 2023-09-05 00:00:00

Even though dyadic regressions are widely used in empirical applications, the (asymptotic) properties of estimation methods only began to be studied recently in the literature. This paper aims to provide in a step-by-step manner how U-statistics tools can be applied to obtain the asymptotic properties of pairwise differences estimators for a two-way fixed effects model of dyadic interactions. More specifically, we first propose an estimator for the model that relies on pairwise differencing such that the fixed effects are differenced out. As a result, the summands of the influence function will not be independent anymore, showing dependence on the individual level and translating to the fact that the usual law of large numbers and central limit theorems do not straightforwardly apply. To overcome such obstacles, we show how to generalize tools of U-statistics for single-index variables to the double-indices context of dyadic datasets. A key result is that there can be different ways of defining the Hajek projection for a directed dyadic structure, which will lead to distinct, but equivalent, consistent estimators for the asymptotic variances. The results presented in this paper are easily extended to non-linear models.
The Local Projection Residual Bootstrap for AR(1) Models
Authors: Amilcar Velez
Date: 2023-09-05 00:00:00
Tasks: valid

This paper proposes a local projection residual bootstrap method to construct confidence intervals for impulse response coefficients of AR(1) models. Our bootstrap method is based on the local projection (LP) approach and involves a residual bootstrap procedure applied to AR(1) models. We present theoretical results for our bootstrap method and proposed confidence intervals. First, we prove the uniform consistency of the LP-residual bootstrap over a large class of AR(1) models that allow for a unit root, conditional heteroskedasticity of unknown form, and serially dependent shocks. Then, we prove the asymptotic validity of our confidence intervals over the same class of AR(1) models. Finally, we show that the LP-residual bootstrap provides asymptotic refinements for confidence intervals on a restricted class of AR(1) models relative to those required for the uniform consistency of our bootstrap.
Design-Based Multi-Way Clustering
Authors: Luther Yap
Date: 2023-09-04 00:00:00
Tasks: Clustering, valid

This paper extends the design-based framework to settings with multi-way cluster dependence, and shows how multi-way clustering can be justified when clustered assignment and clustered sampling occurs on different dimensions, or when either sampling or assignment is multi-way clustered. Unlike one-way clustering, the plug-in variance estimator in multi-way clustering is no longer conservative, so valid inference either requires an assumption on the correlation of treatment effects or a more conservative variance estimator. Simulations suggest that the plug-in variance estimator is usually robust, and the conservative variance estimator is often too conservative.
The Robust F-Statistic as a Test for Weak Instruments
Authors: Frank Windmeijer
Date: 2023-09-04 00:00:00

Montiel Olea and Pflueger (2013) proposed the effective F-statistic as a test for weak instruments in terms of the Nagar bias of the two-stage least squares (2SLS) estimator relative to a benchmark worst-case bias. We show that their methodology applies to a class of linear generalized method of moments (GMM) estimators with an associated class of generalized effective F-statistics. The standard nonhomoskedasticity robust F-statistic is a member of this class. The associated GMMf estimator, with the extension f for first-stage, is a novel and unusual estimator as the weight matrix is based on the first-stage residuals. As the robust F-statistic can also be used as a test for underidentification, expressions for the calculation of the weak-instruments critical values in terms of the Nagar bias of the GMMf estimator relative to the benchmark simplify and no simulation methods or Patnaik (1949) distributional approximations are needed. In the grouped-data IV designs of Andrews (2018), where the robust F-statistic is large but the effective F-statistic is small, the GMMf estimator is shown to behave much better in terms of bias than the 2SLS estimator, as expected by the weak-instruments test results.
Moment-Based Estimation of Diffusion and Adoption Parameters in Networks
Authors: L. S. Sanna Stephan
Date: 2023-09-04 00:00:00

According to standard econometric theory, Maximum Likelihood estimation (MLE) is the efficient estimation choice, however, it is not always a feasible one. In network diffusion models with unobserved signal propagation, MLE requires integrating out a large number of latent variables, which quickly becomes computationally infeasible even for moderate network sizes and time horizons. Limiting the model time horizon on the other hand entails loss of important information while approximation techniques entail a (small) error that. Searching for a viable alternative is thus potentially highly beneficial. This paper proposes two estimators specifically tailored to the network diffusion model of partially observed adoption and unobserved network diffusion.
A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model
Authors: L. S. Sanna Stephan
Date: 2023-09-04 00:00:00

Network diffusion models are applicable to many socioeconomic interactions, yet network interaction is hard to observe or measure. Whenever the diffusion process is unobserved, the number of possible realizations of the latent matrix that captures agents' diffusion statuses grows exponentially with the size of network. Due to interdependencies, the log likelihood function can not be factorized in individual components. As a consequence, exact estimation of latent diffusion models with more than one round of interaction is computationally infeasible. In the present paper, I propose a trimming estimator that enables me to establish and maximize an approximate log likelihood function that almost exactly identifies the peak of the true log likelihood function whenever no more than one third of eligible agents are subject to trimming.
iCOS: Option-Implied COS Method
Authors: Evgenii Vladimirov
Date: 2023-09-02 00:00:00

This paper proposes the option-implied Fourier-cosine method, iCOS, for non-parametric estimation of risk-neutral densities, option prices, and option sensitivities. The iCOS method leverages the Fourier-based COS technique, proposed by Fang and Oosterlee (2008), by utilizing the option-implied cosine series coefficients. Notably, this procedure does not rely on any model assumptions about the underlying asset price dynamics, it is fully non-parametric, and it does not involve any numerical optimization. These features make it rather general and computationally appealing. Furthermore, we derive the asymptotic properties of the proposed non-parametric estimators and study their finite-sample behavior in Monte Carlo simulations. Our empirical analysis using S&P 500 index options and Amazon equity options illustrates the effectiveness of the iCOS method in extracting valuable information from option prices under different market conditions. Additionally, we apply our methodology to dissect and quantify observation and discretization errors in the VIX index.
Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making
Authors: Patrick Rehill, Nicholas Biddle
Date: 2023-09-02 00:00:00
Tasks: Decision Making, Fairness, Heterogeneous Treatment Effect Estimation

Causal machine learning methods which flexibly generate heterogeneous treatment effect estimates could be very useful tools for governments trying to make and implement policy. However, as the critical artificial intelligence literature has shown, governments must be very careful of unintended consequences when using machine learning models. One way to try and protect against unintended bad outcomes is with AI Fairness methods which seek to create machine learning models where sensitive variables like race or gender do not influence outcomes. In this paper we argue that standard AI Fairness approaches developed for predictive machine learning are not suitable for all causal machine learning applications because causal machine learning generally (at least so far) uses modelling to inform a human who is the ultimate decision-maker while AI Fairness approaches assume a model that is making decisions directly. We define these scenarios as indirect and direct decision-making respectively and suggest that policy-making is best seen as a joint decision where the causal machine learning model usually only has indirect power. We lay out a definition of fairness for this scenario - a model that provides the information a decision-maker needs to accurately make a value judgement about just policy outcomes - and argue that the complexity of causal machine learning models can make this difficult to achieve. The solution here is not traditional AI Fairness adjustments, but careful modelling and awareness of some of the decision-making biases that these methods might encourage which we describe.
Agriculture Credit and Economic Growth in Bangladesh: A Time Series Analysis
Authors: Md. Toaha, Laboni Mondal
Date: 2023-09-08 00:00:00
Tasks: Time Series, Time Series Analysis

The paper examined the impact of agricultural credit on economic growth in Bangladesh. The annual data of agriculture credit were collected from annual reports of the Bangladesh Bank and other data were collected from the world development indicator (WDI) of the World Bank. By employing Johansen cointegration test and vector error correction model (VECM), the study revealed that there exists a long run relationship between the variables. The results of the study showed that agriculture credit had a positive impact on GDP growth in Bangladesh. The study also found that gross capital formation had a positive, while inflation had a negative association with economic growth in Bangladesh. Therefore, the government and policymakers should continue their effort to increase the volume of agriculture credit to achieve sustainable economic growth.
Motives for Delegating Financial Decisions
Authors: Mikhail Freer, Daniel Friedman, Simon Weidenholzer
Date: 2023-09-07 00:00:00

Why do some investors delegate financial decisions to supposed experts? We report a laboratory experiment designed to disentangle four possible motives. Almost 600 investors drawn from the Prolific subject pool choose whether or not to delegate a real-stakes choice among lotteries to a previous investor (an ``expert'') after seeing information on the performance of several available experts. We find that a surprisingly large fraction of investors delegate even trivial choice tasks, suggesting a major role for the blame shifting motive. A larger fraction of investors delegate our more complex tasks, suggesting that decision costs play a role for some investors. Some investors who delegate choose a low quality expert with high earnings, suggesting a role for chasing past performance. We find no evidence for a fourth possible motive, that delegation makes risk more acceptable.
Sources of capital growth
Authors: Gordon Getty, Nikita Tkachenko
Date: 2023-09-06 00:00:00

Data from national accounts show no effect of change in net saving or consumption, in ratio to market-value capital, on change in growth rate of market-value capital (capital acceleration). Thus it appears that capital growth and acceleration arrive without help from net saving or consumption restraint. We explore ways in which this is possible, and discuss implications for economic teaching and public policy
Urban Mobility in the Age of Automation: Analyzing Public Attitudes Toward Privately-Owned versus Shared Automated Vehicles
Authors: Yellitza Soto, Fatemeh Nazari, Mohamadhossein Noruzoliaee
Date: 2023-09-06 00:00:00

Transportation systems will be likely transformed by the emergence of automated vehicles (AVs) promising for safe, convenient, and efficient mobility, especially if used in shared systems (shared AV or SAV). However, the potential tendency is observed towards owning AV as a private asset rather than using SAV. This calls for a research on investigating individuals' attitude towards AV in comparison with SAV to recognize the barriers to the public's tendency towards SAV. To do so, the present study proposes a modeling framework based on the theories in behavioral psychology to explain individuals' preference for owning AV over using SAV, built as a latent (subjective) psychometric construct, by three groups of explanatory latent constructs including: (i) desire for searching for benefits, i.e., extrinsic motive manifested in utilitarian beliefs; (ii) tendency towards seeking pleasure and joy, i.e., intrinsic motive reflected in hedonic beliefs; and (iii) attitude towards three configurations of shared mobility, i.e., experience with car and ridesharing, bikesharing, and public transit. Estimated on a sample dataset from the State of California, the findings can shed initial lights on the psychological determinants of the public's attitude towards owning AV versus using SAV, which can furthermore provide policy implications intriguing for policy makers and stakeholders. Of note, the findings reveal the strongest influential factor on preference for AV over SAV as hedonic beliefs reflected in perceived enjoyment. This preference is next affected by utilitarian beliefs, particularly perceived benefit and trust of stranger, followed by attitude towards car and ride sharing.
Proofs for the New Definitions in Financial Markets
Authors: Atilla Aras
Date: 2023-09-06 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, valid

Constructing theorems can help to determine the shape of certain utility curves that make up the new definitions in financial markets. The aim of this study was to present proofs for these theorems. Basic thoughts of new alternative definitions emerge from the decision-making under uncertainty in economics and finance. Shape of the certain utility curve is central to standard definitions in determining risk attitudes of investors. Shape alone determines risk behavior of investors in standard theory. Although the terms risk-averse, risk-loving, and risk-neutral are equivalent to strict concavity, strict convexity, and linearity, respectively, in standard theory, strict concavity or strict convexity, or linearity are valid for certain new definitions, not being the same as standard theory. Hence, it can be stated that new alternative definitions are broader than standard definitions from the viewpoint of shape. For instance, the certain utility curve of a risk-averse investor can be strictly concave or strictly convex, or linear in alternative definitions.
Projections of Economic Impacts of Climate Change on Marine Protected Areas: Palau, the Great Barrier Reef, and the Bering Sea
Authors: Talya ten Brink
Date: 2023-09-05 00:00:00
Tasks: Decision Making, Diversity

Climate change substantially impacts ecological systems. Marine species are shifting their distribution because of climate change towards colder waters, potentially compromising the benefits of currently established Marine Protected Areas (MPAs). Therefore, we demonstrate how three case study regions will be impacted by warming ocean waters to prepare stakeholders to understand how the fisheries around the MPAs is predicted to change. We chose the case studies to focus on large scale MPAs in i) a cold, polar region, ii) a tropical region near the equator, and iii) a tropical region farther from the equator. We quantify the biological impacts of shifts in species distribution due to climate change for fishing communities that depend on the Palau National Marine Sanctuary, the Great Barrier Reef Marine National Park Zone, and the North Bering Sea Research Area MPAs. We find that fisheries sectors will be impacted differently in different regions and show that all three regions can be supported by this methodology for decision making that joins sector income and species diversity.
Dual Effects of the US-China Trade War and COVID-19 on United States Imports: Transfer of China's industrial chain?
Authors: Wei Luo, Siyuan Kang, Sheng Hu, Lixian Su, Rui Dai
Date: 2023-09-05 00:00:00

The trade tension between the U.S. and China since 2018 has caused a steady decoupling of the world's two largest economies. The pandemic outbreak in 2020 complicated this process and had numerous unanticipated repercussions. This paper investigates how U.S. importers reacted to the trade war and worldwide lockdowns due to the COVID-19 pandemic. We examine the effects of the two incidents on U.S. imports separately and collectively, with various economic scopes. Our findings uncover intricate trading dynamics among the U.S., China, and Southeast Asia, through which businesses relocated portions of their global supply chain away from China to avoid high tariffs. Our analysis indicates that increased tariffs cause the U.S. to import less from China. Meanwhile, Southeast Asian exporters have integrated more into value chains centered on Chinese suppliers by participating more in assembling and completing products. However, the worldwide lockdowns over pandemic have reversed this trend as, over this period, the U.S. effectively imported more goods directly from China and indirectly through Southeast Asian exporters that imported from China.
There is power in general equilibrium
Authors: Juan Jacobo
Date: 2023-09-02 00:00:00

The article develops a general equilibrium model where power relations are central in the determination of unemployment, profitability, and income distribution. The paper contributes to the market forces versus institutions debate by providing a unified model capable of identifying key interrelations between technical and institutional changes in the economy. Empirically, the model is used to gauge the relative roles of technology and institutions in the behavior of the labor share, the unemployment rate, the capital-output ratio, and business profitability and demonstrates how they complement each other in providing an adequate narrative to the structural changes of the US economy.
Economic Complexity Limits Accuracy of Price Probability Predictions by Gaussian Distributions
Authors: Victor Olkhov
Date: 2023-08-24 00:00:00

We discuss the economic reasons why the predictions of price and return statistical moments in the coming decades, in the best case, will be limited by their averages and volatilities. That limits the accuracy of the forecasts of price and return probabilities by Gaussian distributions. The economic origin of these restrictions lies in the fact that the predictions of the market-based n-th statistical moments of price and return for n=1,2,.., require the description of the economic variables of the n-th order that are determined by sums of the n-th degrees of values or volumes of market trades. The lack of existing models that describe the evolution of the economic variables determined by the sums of the 2nd degrees of market trades results in the fact that even predictions of the volatilities of price and return are very uncertain. One can ignore existing economic barriers that we highlight but cannot overcome or resolve them. The accuracy of predictions of price and return probabilities substantially determines the reliability of asset pricing models and portfolio theories. The restrictions on the accuracy of predictions of price and return statistical moments reduce the reliability and veracity of modern asset pricing and portfolio theories.