cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.10115480
title: The Impact of Food Prices on Conflict Revisited

description: Studies that examine the impact of food prices on conflict usually assume that (all) changes in international food prices are exogenous shocks for individual countries or local areas. By isolating strictly exogenous shifts in global food commodity prices, we show that this assumption could seriously distort estimations of the impact on conflict in African regions. Specifically, we show that increases in food prices that are caused by harvest shocks outside Africa raise conflict significantly, whereas a “naive” regression of conflict on international food prices uncovers an inverse relationship. We also find that higher food prices lead to more conflict in regions with more agricultural production. Again, we document that failing to account for exogenous price changes exhibits a considerable bias in the impact. In addition, we show that the conventional approach to evaluate such effects; that is, estimations that include time fixed effects, ignores an important positive baseline effect that is common for all regions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9816671.v2
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.9879872
title: Semiparametric GARCH via Bayesian Model Averaging

description: As the dynamic structure of financial markets is subject to dramatic change, a model capable of providing consistently accurate volatility estimates should not make rigid assumptions on how prices change over time. Most volatility models impose a particular parametric functional form that relates an observed price change to a volatility forecast (news impact function). Here, a new class of functional coefficient semiparametric volatility models is proposed, where the news impact function is allowed to be any smooth function. The ability of the proposed model to estimate volatility is studied and compared to the well-known parametric proposals, in both a simulation study and an empirical study with real financial market data. The news impact function is estimated using a Bayesian model averaging approach, implemented via a carefully developed Markov chain Monte Carlo sampling algorithm. Using simulations it is shown that the proposed flexible semiparametric model is able to learn the shape of the news impact function very effectively, from observed data. When applied to real financial time series, the proposed model suggests that news impact functions have quite different shapes over different asset types, but a consistent shape within the same asset class. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10262594
title: Equality-Minded Treatment Choice

description: The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A policy maximizing the sum of individual incomes may not be desirable if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This article develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy objective is to maximize an <i>equality-minded rank-dependent social welfare function</i>, which puts higher weight on individuals with lower-ranked outcomes. We estimate the optimal policy by maximizing a sample analog of the rank-dependent welfare over a properly constrained set of policies. We show that the average social welfare attained by our estimated policy converges to the maximal attainable welfare at n−1/2 rate uniformly over a large class of data distributions when the propensity score is known. We also show that this rate is minimax optimal. We provide an application of our method using the data from the National JTPA Study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879881.v3
title: The Evolving Impact of Global, Region-Specific, and Country-Specific Uncertainty

description: We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it using a large panel of macroeconomic and financial data for 22 countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (“global uncertainty”), region-specific uncertainty, and country-specific uncertainty. Among other findings, the estimates suggest that global uncertainty plays a primary role in explaining the volatility of inflation, interest rates, and stock prices, although to a varying extent over time, while all uncertainty components are found to play a nonnegligible role for real economic activity, credit, and money for most countries. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10022810.v2
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.10115480.v3
title: The Impact of Food Prices on Conflict Revisited

description: Studies that examine the impact of food prices on conflict usually assume that (all) changes in international food prices are exogenous shocks for individual countries or local areas. By isolating strictly exogenous shifts in global food commodity prices, we show that this assumption could seriously distort estimations of the impact on conflict in African regions. Specifically, we show that increases in food prices that are caused by harvest shocks outside Africa raise conflict significantly, whereas a “naive” regression of conflict on international food prices uncovers an inverse relationship. We also find that higher food prices lead to more conflict in regions with more agricultural production. Again, we document that failing to account for exogenous price changes exhibits a considerable bias in the impact. In addition, we show that the conventional approach to evaluate such effects; that is, estimations that include time fixed effects, ignores an important positive baseline effect that is common for all regions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10262594.v4
title: Equality-Minded Treatment Choice

description: The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A policy maximizing the sum of individual incomes may not be desirable if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This article develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy objective is to maximize an <i>equality-minded rank-dependent social welfare function</i>, which puts higher weight on individuals with lower-ranked outcomes. We estimate the optimal policy by maximizing a sample analog of the rank-dependent welfare over a properly constrained set of policies. We show that the average social welfare attained by our estimated policy converges to the maximal attainable welfare at n−1/2 rate uniformly over a large class of data distributions when the propensity score is known. We also show that this rate is minimax optimal. We provide an application of our method using the data from the National JTPA Study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9976895.v2
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.9879881
title: The Evolving Impact of Global, Region-Specific, and Country-Specific Uncertainty

description: We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it using a large panel of macroeconomic and financial data for 22 countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (“global uncertainty”), region-specific uncertainty, and country-specific uncertainty. Among other findings, the estimates suggest that global uncertainty plays a primary role in explaining the volatility of inflation, interest rates, and stock prices, although to a varying extent over time, while all uncertainty components are found to play a nonnegligible role for real economic activity, credit, and money for most countries. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10276022
title: Copula-Based Random Effects Models for Clustered Data

description: In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that models the correlation of the unobserved heterogeneity among individuals in the same cluster using a copula. I discuss the asymptotic efficiency of the estimator relative to standard random effects estimators, and to choose the copula I propose a specification test. The implementation of the estimator requires the numerical approximation of high-dimensional integrals, for which I propose an algorithm that works for Archimedean copulas that does not suffer from the curse of dimensionality. This method is illustrated with an application of labor supply in married couples, finding that about one half of the difference in probability of a woman being employed when her husband is also employed, relative to those whose husband is unemployed, is explained by correlation in the unobservables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10276022.v2
title: Copula-Based Random Effects Models for Clustered Data

description: In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that models the correlation of the unobserved heterogeneity among individuals in the same cluster using a copula. I discuss the asymptotic efficiency of the estimator relative to standard random effects estimators, and to choose the copula I propose a specification test. The implementation of the estimator requires the numerical approximation of high-dimensional integrals, for which I propose an algorithm that works for Archimedean copulas that does not suffer from the curse of dimensionality. This method is illustrated with an application of labor supply in married couples, finding that about one half of the difference in probability of a woman being employed when her husband is also employed, relative to those whose husband is unemployed, is explained by correlation in the unobservables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879869
title: Measuring Asset Market Linkages: Nonlinear Dependence and Tail Risk

description: Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail risk. Motivated by these limitations, we introduce the (tail-restricted) integrated regression function, and we show how it characterizes conditional dependence and persistence. We propose simple estimates for these measures and establish their asymptotic properties. We employ the proposed methods to analyze the dependence structure of some of the major international stock market indices before, during, and after the 2007–2009 financial crisis. Monte Carlo simulations and the application show that our new measures are more reliable and accurate than competing methods based on MES or exceedance correlations for testing tail dependence. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879869.v2
title: Measuring Asset Market Linkages: Nonlinear Dependence and Tail Risk

description: Traditional measures of dependence in time series are based on correlations or periodograms. These are adequate in many circumstances but, in others, especially when trying to assess market linkages and tail risk during abnormal times (e.g., financial contagion), they might be inappropriate. In particular, popular tail dependence measures based on exceedance correlations and marginal expected shortfall (MES) have large variances and also contain limited information on tail risk. Motivated by these limitations, we introduce the (tail-restricted) integrated regression function, and we show how it characterizes conditional dependence and persistence. We propose simple estimates for these measures and establish their asymptotic properties. We employ the proposed methods to analyze the dependence structure of some of the major international stock market indices before, during, and after the 2007–2009 financial crisis. Monte Carlo simulations and the application show that our new measures are more reliable and accurate than competing methods based on MES or exceedance correlations for testing tail dependence. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879881.v2
title: The Evolving Impact of Global, Region-Specific, and Country-Specific Uncertainty

description: We develop a dynamic factor model with time-varying parameters and stochastic volatility, estimate it using a large panel of macroeconomic and financial data for 22 countries and decompose the variance of each variable in terms of contributions from uncertainty common to all countries (“global uncertainty”), region-specific uncertainty, and country-specific uncertainty. Among other findings, the estimates suggest that global uncertainty plays a primary role in explaining the volatility of inflation, interest rates, and stock prices, although to a varying extent over time, while all uncertainty components are found to play a nonnegligible role for real economic activity, credit, and money for most countries. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10115480.v2
title: The Impact of Food Prices on Conflict Revisited

description: Studies that examine the impact of food prices on conflict usually assume that (all) changes in international food prices are exogenous shocks for individual countries or local areas. By isolating strictly exogenous shifts in global food commodity prices, we show that this assumption could seriously distort estimations of the impact on conflict in African regions. Specifically, we show that increases in food prices that are caused by harvest shocks outside Africa raise conflict significantly, whereas a “naive” regression of conflict on international food prices uncovers an inverse relationship. We also find that higher food prices lead to more conflict in regions with more agricultural production. Again, we document that failing to account for exogenous price changes exhibits a considerable bias in the impact. In addition, we show that the conventional approach to evaluate such effects; that is, estimations that include time fixed effects, ignores an important positive baseline effect that is common for all regions. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10262594.v3
title: Equality-Minded Treatment Choice

description: The goal of many randomized experiments and quasi-experimental studies in economics is to inform policies that aim to raise incomes and reduce economic inequality. A policy maximizing the sum of individual incomes may not be desirable if it magnifies economic inequality and post-treatment redistribution of income is infeasible. This article develops a method to estimate the optimal treatment assignment policy based on observable individual covariates when the policy objective is to maximize an <i>equality-minded rank-dependent social welfare function</i>, which puts higher weight on individuals with lower-ranked outcomes. We estimate the optimal policy by maximizing a sample analog of the rank-dependent welfare over a properly constrained set of policies. We show that the average social welfare attained by our estimated policy converges to the maximal attainable welfare at n−1/2 rate uniformly over a large class of data distributions when the propensity score is known. We also show that this rate is minimax optimal. We provide an application of our method using the data from the National JTPA Study. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10059779
title: Sharp Bounds on Functionals of the Joint Distribution in the Analysis of Treatment Effects

description: This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate additional model constraints. However, computational considerations for the method are found to be important and are discussed in detail. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10276022.v1
title: Copula-Based Random Effects Models for Clustered Data

description: In a binary choice panel data framework, probabilities of the outcomes of several individuals depend on the correlation of the unobserved heterogeneity. I propose a random effects estimator that models the correlation of the unobserved heterogeneity among individuals in the same cluster using a copula. I discuss the asymptotic efficiency of the estimator relative to standard random effects estimators, and to choose the copula I propose a specification test. The implementation of the estimator requires the numerical approximation of high-dimensional integrals, for which I propose an algorithm that works for Archimedean copulas that does not suffer from the curse of dimensionality. This method is illustrated with an application of labor supply in married couples, finding that about one half of the difference in probability of a woman being employed when her husband is also employed, relative to those whose husband is unemployed, is explained by correlation in the unobservables. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.10059779.v3
title: Sharp Bounds on Functionals of the Joint Distribution in the Analysis of Treatment Effects

description: This article proposes an identification and estimation method that allows researchers to bound continuous functionals of the joint distribution of potential outcomes from the literature on treatment effects. The focus is on a model where no restrictions are imposed on treatment selection. The method can sharply bound interesting parameters when analytical bounds are difficult to derive, can be used in settings in which instruments are available, and can easily accommodate additional model constraints. However, computational considerations for the method are found to be important and are discussed in detail. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879857
title: A Framework for Separating Individual-Level Treatment Effects From Spillover Effects

description: This article suggests a causal framework for separating individual-level treatment effects and spillover effects such as general equilibrium, interference, or interaction effects related to treatment distribution. We relax the stable unit treatment value assumption assuming away treatment-dependent interaction between study participants and permit spillover effects within aggregates, for example, regions. Based on our framework, we systematically categorize the individual-level and spillover effects considered in the previous literature and clarify the assumptions required for identification under different designs, for instance, based on randomization or selection on observables. Furthermore, we propose a novel difference-in-differences approach and apply it to a policy intervention extending unemployment benefit durations in selected regions of Austria that arguably affected ineligibles in treated regions through general equilibrium effects in local labor markets.