cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.8263328
title: Focused Information Criterion and Model Averaging for Large Panels With a Multifactor Error Structure

description: This article considers model selection and model averaging in panel data models with a multifactor error structure. We investigate the limiting distribution of the common correlated effects estimator in a local asymptotic framework and show that the trade-off between bias and variance remains in asymptotic theory. We then propose a focused information criterion and a plug-in averaging estimator for large heterogeneous panels and examine their theoretical properties. The novel feature of the proposed method is that it aims to minimize the sample analog of the asymptotic mean squared error and can be applied to cases irrespective of whether the rank condition holds or not. Monte Carlo simulations show that both proposed selection and averaging methods generally achieve lower mean squared error than other methods. The proposed methods are applied to examine possible causes that lead to the increasing wage inequality between high-skilled and low-skilled workers in the U.S. manufacturing industries.
DOI: 10.6084/m9.figshare.8263328.v2
title: Focused Information Criterion and Model Averaging for Large Panels With a Multifactor Error Structure

description: This article considers model selection and model averaging in panel data models with a multifactor error structure. We investigate the limiting distribution of the common correlated effects estimator in a local asymptotic framework and show that the trade-off between bias and variance remains in asymptotic theory. We then propose a focused information criterion and a plug-in averaging estimator for large heterogeneous panels and examine their theoretical properties. The novel feature of the proposed method is that it aims to minimize the sample analog of the asymptotic mean squared error and can be applied to cases irrespective of whether the rank condition holds or not. Monte Carlo simulations show that both proposed selection and averaging methods generally achieve lower mean squared error than other methods. The proposed methods are applied to examine possible causes that lead to the increasing wage inequality between high-skilled and low-skilled workers in the U.S. manufacturing industries.
DOI: 10.6084/m9.figshare.8940344.v1
title: Multi-Horizon Forecast Comparison

description: We introduce tests for multi-horizon superior predictive ability (SPA). Rather than comparing forecasts of different models at multiple horizons individually, we propose to jointly consider all horizons of a forecast path. We define the concepts of uniform and average SPA. The former entails superior performance at each individual horizon, while the latter allows inferior performance at some horizons to be compensated by others. The article illustrates how the tests lead to more coherent conclusions, and how they are better able to differentiate between models than the single-horizon tests. We provide an extension of the previously introduced model confidence set to allow for multi-horizon comparison of more than two models. Simulations demonstrate appropriate size and high power. An illustration of the tests on a large set of macroeconomic variables demonstrates the empirical benefits of multi-horizon comparison.
DOI: 10.6084/m9.figshare.9105017.v2
title: Exploring Encouragement, Treatment, and Spillover Effects Using Principal Stratification, With Application to a Field Experiment on Teens’ Museum Attendance

description: This article revisits results from a field experiment, conducted in Florence, Italy, to study the effects of incentives provided to high school teens to motivate them to visit art museums. In the experiment, different classes of students were randomized to three types of encouragement and were offered a free visit to a main museum in the city. Using the principal stratification framework, the article explores causal pathways that may lead students to increase future visits, as induced by the encouragement received, or by the individual experience of the proposed free museum visit, or by the spillover of classmates’ experience. We do so by estimating and interpreting the causal effects of the three forms of encouragement within the principal strata defined by compliance behaviors. Bayesian inferential methods are used to derive the posterior distributions of weakly identified causal parameters.
DOI: 10.6084/m9.figshare.9677687.v4
title: Testing for Changes in Forecasting Performance

description: We consider the issue of forecast failure (or breakdown) and propose methods to assess retrospectively whether a given forecasting model provides forecasts which show evidence of changes with respect to some loss function. We adapt the classical structural change tests to the forecast failure context. First, we recommend that all tests should be carried with a fixed scheme to have best power. This ensures a maximum difference between the fitted in and out-of-sample means of the losses and avoids contamination issues under the rolling and recursive schemes. With a fixed scheme, Giacomini and Rossi’s (GR) test is simply a Wald test for a one-time change in the mean of the total (the in-sample plus out-of-sample) losses at a known break date, say <i>m</i>, the value that separates the in and out-of-sample periods. To alleviate this problem, we consider a variety of tests: maximizing the GR test over values of <i>m</i> within a prespecified range; a Double sup-Wald (DSW) test which for each <i>m</i> performs a sup-Wald test for a change in the mean of the out-of-sample losses and takes the maximum of such tests over some range; we also propose to work directly with the total loss series to define the Total Loss sup-Wald and Total Loss UDmax (TLUD) tests. Using theoretical analyses and simulations, we show that with forecasting models potentially involving lagged dependent variables, the only tests having a monotonic power function for all data-generating processes considered are the DSW and TLUD tests, constructed with a fixed forecasting window scheme. Some explanations are provided and empirical applications illustrate the relevance of our findings in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.8263328.v1
title: Focused Information Criterion and Model Averaging for Large Panels with a Multifactor Error Structure

description: This paper considers model selection and model averaging in panel data models with a multifactor error structure. We investigate the limiting distribution of the common correlated effects estimator (Pesaran, 2006) in a local asymptotic framework and show that the trade-off between bias and variance remains in asymptotic theory. We then propose a focused information criterion and a plug-in averaging estimator for large heterogeneous panels and examine their theoretical properties. The novel feature of the proposed method is that it aims to minimize the sample analog of the asymptotic mean squared error and can be applied to cases irrespective of whether the rank condition holds or not. Monte Carlo simulations show that both proposed selection and averaging methods generally achieve lower mean squared error than other methods. The proposed methods are applied to examine possible causes that lead to the increasing wage inequality between high-skilled and low-skilled workers in the U.S. manufacturing industries.
DOI: 10.6084/m9.figshare.9105026.v3
title: Dealing With Endogeneity in Threshold Models Using Copulas

description: We suggest a new method dealing with the problem of endogeneity of the threshold variable in structural threshold regression models based on copula theory. This method enables us to relax the assumption that the threshold variable is normally distributed and to capture the dependence structure between the threshold regression error term and the threshold variable independently of the marginal distribution of the threshold variable. For Gaussian and Student’s <i>t</i> copulas, this dependent structure can be captured by copula-type transformations of the distribution of the threshold variable, for each regime of the model. Augmenting the threshold model under these transformations can control for the endogeneity problem of threshold variable. The single-factor correlation structure of the threshold regression error term with these transformations allows us to consistently estimate the threshold and the slope parameters of the model based on a least squares method. Based on a Monte Carlo study, we show that our method is robust to nonlinear dependence structures between the regression error term and the threshold variable implied by the Archimedean family of copulas. We illustrate the method by estimating a model of the foreign-trade multiplier for seven OECD economies.
DOI: 10.6084/m9.figshare.9105026
title: Dealing With Endogeneity in Threshold Models Using Copulas

description: We suggest a new method dealing with the problem of endogeneity of the threshold variable in structural threshold regression models based on copula theory. This method enables us to relax the assumption that the threshold variable is normally distributed and to capture the dependence structure between the threshold regression error term and the threshold variable independently of the marginal distribution of the threshold variable. For Gaussian and Student’s <i>t</i> copulas, this dependent structure can be captured by copula-type transformations of the distribution of the threshold variable, for each regime of the model. Augmenting the threshold model under these transformations can control for the endogeneity problem of threshold variable. The single-factor correlation structure of the threshold regression error term with these transformations allows us to consistently estimate the threshold and the slope parameters of the model based on a least squares method. Based on a Monte Carlo study, we show that our method is robust to nonlinear dependence structures between the regression error term and the threshold variable implied by the Archimedean family of copulas. We illustrate the method by estimating a model of the foreign-trade multiplier for seven OECD economies.
DOI: 10.6084/m9.figshare.9677687.v3
title: Testing for Changes in Forecasting Performance

description: We consider the issue of forecast failure (or breakdown) and propose methods to assess retrospectively whether a given forecasting model provides forecasts which show evidence of changes with respect to some loss function. We adapt the classical structural change tests to the forecast failure context. First, we recommend that all tests should be carried with a fixed scheme to have best power. This ensures a maximum difference between the fitted in and out-of-sample means of the losses and avoids contamination issues under the rolling and recursive schemes. With a fixed scheme, Giacomini and Rossi’s (GR) test is simply a Wald test for a one-time change in the mean of the total (the in-sample plus out-of-sample) losses at a known break date, say <i>m</i>, the value that separates the in and out-of-sample periods. To alleviate this problem, we consider a variety of tests: maximizing the GR test over values of <i>m</i> within a prespecified range; a Double sup-Wald (DSW) test which for each <i>m</i> performs a sup-Wald test for a change in the mean of the out-of-sample losses and takes the maximum of such tests over some range; we also propose to work directly with the total loss series to define the Total Loss sup-Wald and Total Loss UDmax (TLUD) tests. Using theoretical analyses and simulations, we show that with forecasting models potentially involving lagged dependent variables, the only tests having a monotonic power function for all data-generating processes considered are the DSW and TLUD tests, constructed with a fixed forecasting window scheme. Some explanations are provided and empirical applications illustrate the relevance of our findings in practice. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9105026.v2
title: Dealing With Endogeneity in Threshold Models Using Copulas

description: We suggest a new method dealing with the problem of endogeneity of the threshold variable in structural threshold regression models based on copula theory. This method enables us to relax the assumption that the threshold variable is normally distributed and to capture the dependence structure between the threshold regression error term and the threshold variable independently of the marginal distribution of the threshold variable. For Gaussian and Student’s <i>t</i> copulas, this dependent structure can be captured by copula-type transformations of the distribution of the threshold variable, for each regime of the model. Augmenting the threshold model under these transformations can control for the endogeneity problem of threshold variable. The single-factor correlation structure of the threshold regression error term with these transformations allows us to consistently estimate the threshold and the slope parameters of the model based on a least squares method. Based on a Monte Carlo study, we show that our method is robust to nonlinear dependence structures between the regression error term and the threshold variable implied by the Archimedean family of copulas. We illustrate the method by estimating a model of the foreign-trade multiplier for seven OECD economies.
DOI: 10.6084/m9.figshare.9746894
title: Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models

description: In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be consistently estimated, the confidence intervals for the strength of identification are straightforward and computationally easy to calculate, as they are obtained from inverting a chi-squared distribution. Furthermore, they provide more information to researchers on instrument strength than the binary decision offered by tests. Monte Carlo simulations show that the confidence intervals have good, albeit conservative, in some cases, small sample coverage. We illustrate the usefulness of the proposed methods in two empirical situations: the estimation of the intertemporal elasticity of substitution in a linearized Euler equation, and government spending multipliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9746894.v3
title: Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models

description: In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be consistently estimated, the confidence intervals for the strength of identification are straightforward and computationally easy to calculate, as they are obtained from inverting a chi-squared distribution. Furthermore, they provide more information to researchers on instrument strength than the binary decision offered by tests. Monte Carlo simulations show that the confidence intervals have good, albeit conservative, in some cases, small sample coverage. We illustrate the usefulness of the proposed methods in two empirical situations: the estimation of the intertemporal elasticity of substitution in a linearized Euler equation, and government spending multipliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9594299.v1
title: Bias-corrected Common Correlated Effects Pooled estimation in dynamic panels

description: This paper extends the Common Correlated Effects Pooled (CCEP) estimator to homogeneous dynamic panels. In this setting CCEP suffers from a large bias when the time span (T) of the dataset is fixed. We develop a bias-corrected CCEP estimator that is consistent as the number of cross-sectional units (N) tends to infinity, for T fixed or growing large, provided that the specification is augmented with a sufficient number of cross-sectional averages, and lags thereof. Monte Carlo experiments show that the correction offers strong improvements in terms of bias and variance. We apply our approach to estimate the dynamic impact of temperature shocks on aggregate output growth.
DOI: 10.6084/m9.figshare.9746894.v2
title: Confidence Intervals for Bias and Size Distortion in IV and Local Projections-IV Models

description: In this article, we propose methods to construct confidence intervals for the bias of the two-stage least squares estimator, and the size distortion of the associated Wald test in instrumental variables models with heteroscedasticity and serial correlation. Importantly our framework covers the local projections—instrumental variable model as well. Unlike tests for weak instruments, whose distributions are nonstandard and depend on nuisance parameters that cannot be consistently estimated, the confidence intervals for the strength of identification are straightforward and computationally easy to calculate, as they are obtained from inverting a chi-squared distribution. Furthermore, they provide more information to researchers on instrument strength than the binary decision offered by tests. Monte Carlo simulations show that the confidence intervals have good, albeit conservative, in some cases, small sample coverage. We illustrate the usefulness of the proposed methods in two empirical situations: the estimation of the intertemporal elasticity of substitution in a linearized Euler equation, and government spending multipliers. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9976895.v3
title: Wild Bootstrap and Asymptotic Inference With Multiway Clustering

description: We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which <i>t</i>-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of <i>t</i>-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the <i>t</i>-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.
DOI: 10.6084/m9.figshare.10022810
title: A Nodewise Regression Approach to Estimating Large Portfolios

description: This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show that the variance, weights, and risk of the global minimum variance portfolios and the Markowitz mean-variance portfolios are consistently estimated with more assets than observations. We show, empirically, that the nodewise regression-based approach performs well in comparison to factor models and shrinkage methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10022810.v3
title: A Nodewise Regression Approach to Estimating Large Portfolios

description: This article investigates the large sample properties of the variance, weights, and risk of high-dimensional portfolios where the inverse of the covariance matrix of excess asset returns is estimated using a technique called nodewise regression. Nodewise regression provides a direct estimator for the inverse covariance matrix using the least absolute shrinkage and selection operator to estimate the entries of a sparse precision matrix. We show that the variance, weights, and risk of the global minimum variance portfolios and the Markowitz mean-variance portfolios are consistently estimated with more assets than observations. We show, empirically, that the nodewise regression-based approach performs well in comparison to factor models and shrinkage methods. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9816671
title: Homogeneity Pursuit in Single Index Models based Panel Data Analysis

description: Panel data analysis is an important topic in statistics and econometrics. Traditionally, in panel data analysis, all individuals are assumed to share the same unknown parameters, e.g. the same coefficients of covariates when the linear models are used, and the differences between the individuals are accounted for by cluster effects. This kind of modelling only makes sense if our main interest is on the global trend, this is because it would not be able to tell us anything about the individual attributes which are sometimes very important. In this paper, we propose a modelling based on the single index models embedded with homogeneity for panel data analysis, which builds the individual attributes in the model and is parsimonious at the same time. We develop a data driven approach to identify the structure of homogeneity, and estimate the unknown parameters and functions based on the identified structure. Asymptotic properties of the resulting estimators are established. Intensive simulation studies conducted in this paper also show the resulting estimators work very well when sample size is finite. Finally, the proposed modelling is applied to a public financial dataset and a UK climate dataset, the results reveal some interesting findings.
DOI: 10.6084/m9.figshare.9976895
title: Wild Bootstrap and Asymptotic Inference With Multiway Clustering

description: We study two cluster-robust variance estimators (CRVEs) for regression models with clustering in two dimensions and give conditions under which <i>t</i>-statistics based on each of them yield asymptotically valid inferences. In particular, one of the CRVEs requires stronger assumptions about the nature of the intra-cluster correlations. We then propose several wild bootstrap procedures and state conditions under which they are asymptotically valid for each type of <i>t</i>-statistic. Extensive simulations suggest that using certain bootstrap procedures with one of the <i>t</i>-statistics generally performs very well. An empirical example confirms that bootstrap inferences can differ substantially from conventional ones.