pinecone/core-2020-05-10-deduplication huggingface dataset:
Conditional Markov chain and its application in economic time series analysis

Motivated by the great moderation in major U.S. macroeconomic time series, we formulate the regime switching problem through a conditional Markov chain. We model the long-run volatility change as a recurrent structure change, while short-run changes in the mean growth rate as regime switches. Both structure and
regime are unobserved. The structure is assumed to be Markovian. Conditioning on the structure, the regime is also Markovian, whose transition matrix is structure-dependent. This formulation imposes interpretable restrictions on the Hamilton Markov switching model. Empirical studies show that this restricted model well identifies both short-run regime switches and long-run structure changes in the U.S. macroeconomic data.
Factor Analysis of Permanent and Transitory Dynamics of the U.S. Economy and the Stock Market

We analyze dynamics of the permanent and transitory components of the U.S. economic activity and the stock market obtained by multivariate dynamic factor modeling. We capture asymmetries over the phases of economic and stock market trends and cycles using independent Markov-switching processes. We show that both output and stock prices contain significant transitory components, while consumption and dividends are useful to identify their respective permanent components. The extracted economic trend perfectly predicts all post-war recessions. Our results shed light to the nature of the bilateral predictability of the economy and the stock market. The transitory stock market component signals recessions with an average lead of one quarter, whereas the market trend is correlated with the economic trend with varying lead/lag times.
Biases in approximating log production

Most empirical work in economic growth assumes either a Cobb-Douglas production function expressed in logs or a log-approximated constant elasticity of substitution specification.  Estimates from each are likely biased due to logging the model and the latter can also suffer from approximation bias. We illustrate this with a successful replication of Masanjala and Papagerogiou (2004) and then estimate both models in levels to avoid these biases. Our estimation in levels gives results in line with conventional wisdom.
Euro corporate bonds risk factors

This paper investigates the determinants of credit spread changes in Euro-denominated bonds. Because credit spread changes can be easily viewed as an excess return on corporate bonds over treasury bonds, we adopt a factor model framework, inspired by the credit risk structural approach. We try to assess the relative importance of
market and idiosyncratic factors in explaining the movements in credit spreads. We adopt a heterogeneous panel with a multifactor error model and propose a two-step estimation procedure which yields consistent estimates of unobserved factors.
The analysis is carried out with a panel of monthly redemption yields on a set of corporate bonds for a time span of three years. Our results suggest that the Euro corporate market is driven by observable and unobservable factors. Where the latter are identified through a
consistent estimation of individual and common observable effects.
We observe that the factors predicted by the structural model are not as relevant as in the case of the US market. The empirical results also suggest that an unobserved common factor has a significant role in explaining the systematic changes in credit spreads. However, contrary to the American evidence, it cannot be identified as a market factor.
Nonparametric Estimation Of Entry Cost In First-Price Procurement Auctions

In this paper, I investigate Samuelson's low-price auction model with entry costs. The model's equilibrium implies that the distribution of bids is truncated at the threshold for participation. I use the model to estimate the cost of participation in Michigan highway procurement auctions. The null hypothesis of zero entry costs is rejected. Using my empirical results, I then construct an estimate of the optimal auction, which employs regular policy tools such as entry fees. Finally, I demonstrate the savings that the Michigan government could have made on payments if optimal auctions had been employed. © 2012 John Wiley & Sons, Ltd.postprin
Spatial Filtering, Model Uncertainty and the\ud
Speed of Income Convergence in Europe

In this paper we put forward a Bayesian Model Averaging method aimed at performing\ud
inference under model uncertainty in the presence of potential spatial autocorrelation.\ud
The method uses spatial filtering in order to account for uncertainty in\ud
spatial linkages. Our procedure is applied to a dataset of income per capita growth and\ud
50 potential determinants for 255 NUTS-2 European regions. We show that ignoring\ud
uncertainty in the type of spatial weight matrix can have an important effect on the\ud
estimates of the parameters attached to the model covariates. After integrating out\ud
the uncertainty implied by the choice of regressors and spatial links, human capital\ud
investments and transitional dynamics related to income convergence appear as the\ud
most robust determinants of growth at the regional level in Europe. Our results imply\ud
that a quantitatively important part of the income convergence process in Europe is\ud
influenced by spatially correlated growth spillovers
State dependence and heterogeneity in health using a bias-corrected fixed-effects estimator

This paper estimates a dynamic ordered probit model of self-assessed health
with two fi xed effects: one in the linear index equation and one in the cut points.
This robustly controls for heterogeneity in unobserved health status and in reporting behavior, although we cannot separate both sources of heterogeneity. We fi nd
important state dependence effects, and small, but signi cant effects of income and
other socioeconomic variables. Having dynamics and f exibly accounting for unobserved heterogeneity matters for those estimates. We also contribute to the bias
correction literature in nonlinear panel models by comparing and applying two of
the existing proposals to our modelThe first author gratefully acknowledges that this research was supported by a Marie
Curie International Outgoing Fellowship within the 7th European Community Framework Programme, by
grants ECO2009-11165 and SEJ2006-05710 from the Spanish Minister of Education, MCINN (Consolider-
Ingenio2010) and Consejería de Educación de la Comunidad de Madrid (Excelecon project)Publicad
Information in the yield curve: a macro-finance approach

We use a macro-finance model, incorporating macroeconomic and financial factors, to study the term premium in the US bond market. Estimating the model using Bayesian techniques, we find that a single factor explains most of the variation in bond risk premiums. Furthermore, the model-implied risk premiums account for up to 40% of the variability of one- and two-year excess returns. Using the model to decompose yield spreads into an expectations and a term premium component, we find that, although this decomposition does not seem important to forecast economic activity, it is crucial to forecast inflation for most forecasting horizons.status: publishe
Housing markets and financial stability

List of papers I-IV. Papers II. to IV. are removed from the thesis due to publisher restrictions.

Paper I. Anundsen, A. K., & Jansen, E. S. (2013). Self-reinforcing effects between housing prices and credit. Discussion Papers no. 756. Statistics Norway

Paper II. Anundsen, A. K. (2013). Econometric regime shifts and the US subprime bubble. Journal of Applied Econometrics. Early view version. <a ref="http://dx.doi.org/10.1002/jae.2367">doi:10.1002/jae.2367</a> Embargoed until November 28th 2015.

Paper III. Anundsen, A. K., & Christensen, C. H. (2013) Supply restrictions, subprime lending and regional US housing prices. Submitted.

Paper IV. Anundsen, A. K., & Christensen, C. H. (2013) Regional US housing price formation: Does one size fit all? Manuscript
crs: A package for nonparametric spline estimation in R

crs is a library for R written by Jeffrey S. Racine (Maintainer) and Zhenghua Nie. This add-on package provides a collection of functions for spline-based nonparametric estimation of regression functions with both continuous and categorical regressors. Currently, the crs package integrates data-driven methods for selecting the spline degree, the number of knots and the necessary bandwidths for nonparametric conditional mean, IV and quantile regression. A function for multivariate density spline estimation with mixed data is also currently in the works. As a bonus, the authors have also provided the first simple R interface to the NOMAD (‘nonsmooth mesh adaptive direct search’) optimization solver which can be applied to solve other mixed integer optimization problems that future users might find useful in other settings. Although the crs package shares some of the same functionalities as its kernel-based counterpart—the np package by the same author—it currently lacks some of the features the np package provides, such as hypothesis testing and semiparametric estimation. However, what it lacks in breadth, crs makes up in speed. A Monte Carlo experiment in this review uncovers sizable speed gains compared to its np counterpart, with a marginal loss in terms of goodness of fit. Therefore, the package will be extremely useful for applied econometricians interested in employing nonparametric techniques using large amounts of data with a small number of discrete covariates
The Contribution of Structural Break Models to Forecasting Macroeconomic Series

© 2014 John Wiley & Sons, Ltd. This paper compares the forecasting performance of models that have been proposed for forecasting in the presence of structural breaks. They differ in their treatment of the break process, the model applied in each regime and the out-of-sample probability of a break. In an extensive empirical evaluation, we demonstrate the presence of breaks and their importance for forecasting. We find no single model that consistently works best in the presence of breaks. In many cases, the formal modeling of the break process is important in achieving a good forecast performance. However, there are also many cases where rolling window forecasts perform well
Realized beta GARCH : a multivariate GARCH model with realized measures of volatility

We introduce a multivariate generalized autoregressive conditional heteroskedasticity (GARCH) model that incorporates realized measures of variances and covariances. Realized measures extract information about the current levels of volatilities and correlations from high-frequency data, which is particularly useful for modeling financial returns during periods of rapid changes in the underlying covariance structure. When applied to market returns in conjunction with returns on an individual asset, the model yields a dynamic model specification of the conditional regression coefficient that is known as the beta. We apply the model to a large set of assets and find the conditional betas to be far more variable than usually found with rolling-window regressions based exclusively on daily returns. In the empirical part of the paper, we examine the cross-sectional as well as the time variation of the conditional beta series during the financial crises
Identifying the independent sources of consumption variation

By representing a system of budget shares as an approximate factor model we determine its rank, i.e. the number of common functional forms, or factors and we estimate a base of the factor space by means of approximate principal components. We assume that the extracted factors span the same space of basic Engel curves representing the fundamental forces driving consumers’ behaviour. We identify these curves by imposing statistical independence and by studying their dependence on total expenditure using local linear regressions. We prove consistency of the estimates. Using data from the U.K. Family Expenditure Survey from 1977 to 2006, we find strong evidence of two common factors and mixed evidence of a third factor. These are identified as decreasing, increasing, and almost constant Engel curves. The household consumption behaviour is therefore driven by two factors respectively related to necessities (e.g. food), luxuries (e.g. vehicles), and in some cases by a third factor related to goods to which is allocated the same percentage of total budget both by rich and poor households (e.g. housing)
Interconnections between Eurozone and US booms and busts using a Bayesian Panel Markov-Switching VAR model

The proposed Panel Markov-Switching VAR model accommodates changes in low and high data frequencies and incorporates endogenous time-varying transition matrices of country-specific Markov chains, allowing for interconnections. An efficient multimove sampling algorithm draws time-varying Markov-switching chains. Using industrial production growth and credit spread data, several important data features are obtained. Three regimes appear with slow growth becoming persistent in Eurozone. Turning point analysis indicates the US leading the Eurozone cycle. Amplification effects influence recession probabilities for Eurozone countries. A credit shock results in temporary negative industrial production growth in Germany, Spain and US. Core and peripheral countries exist in Eurozone
Forecasting with the Standardized Self-Perturbed Kalman Filter

We propose and study the finite-sample properties of a modified version of the self-perturbed Kalman filter of Park and Jun (1992) for the on-line estimation of models subject to parameter instability. The perturbation term in the updating equation of the state covariance matrix is now weighted by the estimate of the measurement error variance. This avoids the calibration of a design parameter as the perturbation term is scaled by the level of uncertainty in the data. It is shown by Monte Carlo simulations that this perturbation method is associated with a good tracking of the dynamics of the parameters compared to other on-line, classical and Bayesian methods. The standardized self-perturbed Kalman filter is adopted to forecast the equity premium on the S&P 500 index under several model specifications, and determine the extent to which realized volatility\ud
can be used to predict excess returns
Doubly robust uniform confidence band for the conditional average treatment effect function

In this paper, we propose a doubly robust method to estimate the heterogeneity of the average treatment effect with respect to observed covariates of interest. We consider a situation where a large number of covariates are needed for identifying the average treatment effect but the covariates of interest for analyzing heterogeneity are of much lower dimension. Our proposed estimator is doubly robust and avoids the curse of dimensionality. We propose a uniform confidence band that is easy to compute, and we illustrate its usefulness via Monte Carlo experiments and an application to the effects of smoking on birth weights
Combining density forecasts using focused scoring rules

We investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value-at-Risk estimates improve when weights are based on the censored likelihood scoring rule
Indirect inference with time series observed with error

We propose the indirect inference estimator as a consistent method to estimate the parameters of a structural model when the observed series are contaminated by measurement error by considering the noise as a structural feature. We show that the indirect inference estimates are asymptotically biased if the error is neglected. When the condition for identification is satisfied, the structural and measurement error parameters can be consistently estimated. The issues of identification and misspecification of measurement error are discussed in detail. We illustrate the reliability of this procedure in the estimation of stochastic volatility models based on realized volatility measures contaminated by microstructure nois
Sibling spillover effects in school achievement

This paper provides empirical evidence on sibling spillover effects in school achievement using administrative data on 230 thousand siblings in England. We extend previous strategies to identify peer effects by exploiting the variation in school test scores across subjects observed at ages 11 and 16 as well as variation in peer quality between siblings. We find a statistically significant positive spillover effect from the older to the younger sibling. Sibling spillovers account for a non-negligible proportion of the attainment gap between low and higher income pupils in England
Should I Stay or Should I Go? Bayesian Inference in the Threshold Time Varying Parameter (TTVP) Model

We provide a flexible means of estimating time-varying parameter models in a  Bayesian framework. By specifying the state innovations to be characterized trough a threshold process that is driven by the absolute size of parameter changes, our model detects at each point in time whether a given regression coefficient is con stant or time-varying. Moreover, our framework accounts for model uncertainty in a data-based fashion through Bayesian shrinkage priors on the initial values of the states. In a simulation, we show that our model reliably identifies regime shifts in cases where the data generating processes display high, moderate, and low numbers of movements in the regression parameters. Finally, we illustrate the merits of our approach by means of two applications. In the first application we forecast the US equity premium and in the second application we investigate the macroeconomic effects of a US monetary policy shock.Series: Research Report Series / Department of Statistics and Mathematic
Learning about heterogeneity in returns to schooling

Using data from the National Longitudinal Survey of Youth (NLSY) we introduce and estimate various Bayesian hierarchical models that investigate the nature of unobserved heterogeneity in returns to schooling. We consider a variety of possible forms for the heterogeneity, some motivated by previous theoretical and empirical work and some new ones, and let the data decide among the competing specifications. Empirical results indicate that heterogeneity is present in returns to education. Furthermore, we find strong evidence that the heterogeneity follows a continuous rather than a discrete distribution, and that bivariate normality provides a very reasonable description of individual-level heterogeneity in intercepts and returns to schooling
Semiparametric Bayesian inference in multiple equation models

This paper outlines an approach to Bayesian semiparametric regression in multiple equation models which can be used to carry out inference in seemingly unrelated regressions or simultaneous equations models with nonparametric components. The approach treats the points on each nonparametric regression line as unknown parameters and uses a prior on the degree of smoothness of each line to ensure valid posterior inference despite the fact that the number of parameters is greater than the number of observations. We develop an empirical Bayesian approach that allows us to estimate the prior smoothing hyperparameters from the data. An advantage of our semiparametric model is that it is written as a seemingly unrelated regressions model with independent normal-Wishart prior. Since this model is a common one, textbook results for posterior inference, model comparison, prediction and posterior computation are immediately available. We use this model in an application involving a two-equation structural model drawn from the labour and returns to schooling literatures
An algorithm to reduce the occupational space in gender segregation studies

This paper presents an algorithm based on the bootstrap to select an admissible aggregation level, that is, the minimum number of occupational categories that yield a gender segregation value not significantly smaller than that obtained from the large number of occupational categories usually available in any data set. The approach is illustrated using labour force survey data for Spain for the comparison of gender segregation in 1977 and 1992, as well as 1994 and 2000. To measure gender segregation, an additively decomposable segregation index based on the entropy concept is used. Despite a substantial simplification in the size of the occupation space, the decrease in the segregation index is very small and not significant, regardless of the year. Consequently, intertemporal changes in gender segregation can be studied using a greatly reduced classification of occupations that permits an easier interpretation of results.Publicad
How quickly do forecasters incorporate news? Evidence from cross-country surveys

Using forecasts from Consensus Economics Inc., we provide evidence on the efficiency of real GDP growth forecasts by testing whether forecast revisions are uncorrelated. As the forecast data used are multi-dimensional—18 countries, 24 monthly forecasts for the current and the following year and 16 target years—the panel estimation takes into account the complex structure of the variance–covariance matrix due
to propagation of shocks across countries and economic linkages among them. Efficiency is rejected for all 18 countries: forecast revisions show a high degree of serial correlation. We then develop a framework for characterizing the nature of the inefficiency in forecasts. For a smaller set of countries, the G-7, we estimate a VAR model on forecast revisions. The degree of inefficiency, as  mananifested in the serial correlation of forecast revisions, tends to be smaller in forecasts of the USA than in forecasts for European countries. Our framework also shows that one of the sources of the inefficiency in a country’s forecasts is resistance to utilizing foreign news. Thus the quality of forecasts for many of these countries can be significantly improved if forecasters pay more attention to news originating from outside their respective countries. This is particularly the case for Canadian and French forecasts, which would gain by paying greater attention than they do to news from the USA and Germany, respectively.