cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.9879869.v1
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.9879884
title: Gaussian Processes and Bayesian Moment Estimation

description: Given a set of moment restrictions (MRs) that overidentify a parameter <i>θ</i>, we investigate a semiparametric Bayesian approach for inference on <i>θ</i> that does not restrict the data distribution <i>F</i> apart from the MRs. As main contribution, we construct a degenerate Gaussian process prior that, conditionally on <i>θ</i>, restricts the <i>F</i> generated by this prior to satisfy the MRs with probability one. Our prior works even in the more involved case where the number of MRs is larger than the dimension of <i>θ</i>. We demonstrate that the corresponding posterior for <i>θ</i> is computationally convenient. Moreover, we show that there exists a link between our procedure, the generalized empirical likelihood with quadratic criterion and the limited information likelihood-based procedures. We provide a frequentist validation of our procedure by showing consistency and asymptotic normality of the posterior distribution of <i>θ</i>. The finite sample properties of our method are illustrated through Monte Carlo experiments and we provide an application to demand estimation in the airline market.
DOI: 10.6084/m9.figshare.9879884.v3
title: Gaussian Processes and Bayesian Moment Estimation

description: Given a set of moment restrictions (MRs) that overidentify a parameter <i>θ</i>, we investigate a semiparametric Bayesian approach for inference on <i>θ</i> that does not restrict the data distribution <i>F</i> apart from the MRs. As main contribution, we construct a degenerate Gaussian process prior that, conditionally on <i>θ</i>, restricts the <i>F</i> generated by this prior to satisfy the MRs with probability one. Our prior works even in the more involved case where the number of MRs is larger than the dimension of <i>θ</i>. We demonstrate that the corresponding posterior for <i>θ</i> is computationally convenient. Moreover, we show that there exists a link between our procedure, the generalized empirical likelihood with quadratic criterion and the limited information likelihood-based procedures. We provide a frequentist validation of our procedure by showing consistency and asymptotic normality of the posterior distribution of <i>θ</i>. The finite sample properties of our method are illustrated through Monte Carlo experiments and we provide an application to demand estimation in the airline market.
DOI: 10.6084/m9.figshare.10304798.v1
title: Exponential-type GARCH models with linear-in-variance risk premium

description: One of the implications of the intertemporal capital asset pricing model (CAPM) is that the risk premium of the market portfolio is a linear function of its variance. Yet, estimation theory of classical GARCH-in-mean models with linear-in-variance risk premium requires strong assumptions and is incomplete. We show that exponential-type GARCH models such as EGARCH or Log-GARCH are more natural in dealing with linear-in-variance risk premia. For the popular and more difficult case of EGARCH-in-mean, we derive conditions for the existence of a unique stationary and ergodic solution and invertibility following a stochastic recurrence equation approach. We then show consistency and asymptotic normality of the quasi maximum likelihood estimator under weak moment assumptions. An empirical application estimates the dynamic risk premia of a variety of stock indices using both EGARCH-M and Log-GARCH-M models.
DOI: 10.6084/m9.figshare.9879857.v3
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.
DOI: 10.6084/m9.figshare.10059779.v2
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.9879884.v2
title: Gaussian Processes and Bayesian Moment Estimation

description: Given a set of moment restrictions (MRs) that overidentify a parameter <i>θ</i>, we investigate a semiparametric Bayesian approach for inference on <i>θ</i> that does not restrict the data distribution <i>F</i> apart from the MRs. As main contribution, we construct a degenerate Gaussian process prior that, conditionally on <i>θ</i>, restricts the <i>F</i> generated by this prior to satisfy the MRs with probability one. Our prior works even in the more involved case where the number of MRs is larger than the dimension of <i>θ</i>. We demonstrate that the corresponding posterior for <i>θ</i> is computationally convenient. Moreover, we show that there exists a link between our procedure, the generalized empirical likelihood with quadratic criterion and the limited information likelihood-based procedures. We provide a frequentist validation of our procedure by showing consistency and asymptotic normality of the posterior distribution of <i>θ</i>. The finite sample properties of our method are illustrated through Monte Carlo experiments and we provide an application to demand estimation in the airline market.
DOI: 10.6084/m9.figshare.9971264
title: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

description: This article proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility, and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either toward zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a generalized approximate message passing algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9971264.v3
title: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

description: This article proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility, and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either toward zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a generalized approximate message passing algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.9879857.v2
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.
DOI: 10.6084/m9.figshare.9971264.v2
title: High-Dimensional Macroeconomic Forecasting Using Message Passing Algorithms

description: This article proposes two distinct contributions to econometric analysis of large information sets and structural instabilities. First, it treats a regression model with time-varying coefficients, stochastic volatility, and exogenous predictors, as an equivalent high-dimensional static regression problem with thousands of covariates. Inference in this specification proceeds using Bayesian hierarchical priors that shrink the high-dimensional vector of coefficients either toward zero or time-invariance. Second, it introduces the frameworks of factor graphs and message passing as a means of designing efficient Bayesian estimation algorithms. In particular, a generalized approximate message passing algorithm is derived that has low algorithmic complexity and is trivially parallelizable. The result is a comprehensive methodology that can be used to estimate time-varying parameter regressions with arbitrarily large number of exogenous predictors. In a forecasting exercise for U.S. price inflation this methodology is shown to work very well. Supplementary materials for this article are available online.
DOI: 10.18430/m37mzt
title: X-ray diffraction data for the borrelia burgdorferi BBK32-C in complex with an autolytic fragment of human C1r at 4.1A (7MZT)
DOI: 10.6084/m9.figshare.11704236.v12
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.5281/zenodo.5537591
title: WMDtestbed test experiment

description: Test experiment files for the WMDtestbed repository (https://github.com/TEichinger/WMDtestbed/) It contains 50 posts in the 'atheism' newsgroup and 50 posts in the 'space' newsgroup from the 20 newsgroups benchmark dataset (https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html). It additionally contains a label file that matches textfile names with the labels 'atheism' and 'space'.
DOI: 10.5281/zenodo.5537590
title: WMDtestbed test experiment

description: Test experiment files for the WMDtestbed repository (https://github.com/TEichinger/WMDtestbed/) It contains 50 posts in the 'atheism' newsgroup and 50 posts in the 'space' newsgroup from the 20 newsgroups benchmark dataset (https://scikit-learn.org/0.19/datasets/twenty_newsgroups.html). It additionally contains a label file that matches textfile names with the labels 'atheism' and 'space'.
DOI: 10.18430/m37s87
title: X-ray diffraction data for the crystal Structure of Dihydroorotate dehydrogenase from Plasmodium falciparum in complex with Orotate, FMN, and inhibitor NCGC00600348-01 (7S87)
DOI: 10.6084/m9.figshare.11704236
title: Bayesian Inference for Regression Copulas

description: We propose a new semiparametric distributional regression smoother that is based on a copula decomposition of the joint distribution of the vector of response values. The copula is high-dimensional and constructed by inversion of a pseudo regression, where the conditional mean and variance are semiparametric functions of covariates modeled using regularized basis functions. By integrating out the basis coefficients, an implicit copula process on the covariate space is obtained, which we call a “regression copula.” We combine this with a nonparametric margin to define a copula model, where the entire distribution—including the mean and variance—of the response is a smooth semiparametric function of the covariates. The copula is estimated using both Hamiltonian Monte Carlo and variational Bayes; the latter of which is scalable to high dimensions. Using real data examples and a simulation study, we illustrate the efficacy of these estimators and the copula model. In a substantive example, we estimate the distribution of half-hourly electricity spot prices as a function of demand and two time covariates using radial bases and horseshoe regularization. The copula model produces distributional estimates that are locally adaptive with respect to the covariates, and predictions that are more accurate than those from benchmark models. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11866179.v3
title: Dynamic Semiparametric Factor Model With Structural Breaks

description: For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. Importantly, our results hold for both large and small breaks in the factor dependency structure. The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11926278
title: A Correction for Regression Discontinuity Designs With Group-Specific Mismeasurement of the Running Variable

description: When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is heterogeneous across different groups of observations. We develop a novel measurement error correction procedure capable of addressing heterogeneous mismeasurement structures by leveraging auxiliary information. We also provide adjusted asymptotic variance and standard errors that take into consideration the variability introduced by the estimation of nuisance parameters, and honest confidence intervals that account for potential misspecification. Simulations provide evidence that the proposed procedure corrects the bias introduced by heterogeneous measurement error and achieves empirical coverage closer to nominal test size than “naive” alternatives. Two empirical illustrations demonstrate that correcting for measurement error can either reinforce the results of a study or provide a new empirical perspective on the data.
DOI: 10.6084/m9.figshare.11317805.v3
title: High-Frequency Lead-Lag Effects and Cross-Asset Linkages: A Multi-Asset Lagged Adjustment Model

description: Motivated by the empirical evidence of high-frequency lead-lag effects and cross-asset linkages, we introduce a multi-asset price formation model which generalizes standard univariate microstructure models of lagged price adjustment. Econometric inference on such model provides: (i) a unified statistical test for the presence of lead-lag correlations in the latent price process and for the existence of a multi-asset price formation mechanism; (ii) separate estimation of contemporaneous and lagged dependencies; (iii) an unbiased estimator of the integrated covariance of the efficient martingale price process that is robust to microstructure noise, asynchronous trading, and lead-lag dependencies. Through an extensive simulation study, we compare the proposed estimator to alternative approaches and show its advantages in recovering the true lead-lag structure of the latent price process. Our application to a set of NYSE stocks provides empirical evidence for the existence of a multi-asset price formation mechanism and sheds light on its market microstructure determinants. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11866179
title: Dynamic Semiparametric Factor Model With Structural Breaks

description: For the change-point analysis of a high-dimensional time series, we consider a semiparametric model with dynamic structural break factors. With our model, the observations are described by a few low-dimensional factors with time-invariant loading functions of the covariates. Regarding the structural break, the factors are assumed to be nonstationary and follow a vector autoregression process with a change in the parameter values. In addition, to account for the known spatial discrepancies, we introduce discrete loading functions. We study the theoretical properties of the estimates of the loading functions and the factors. Moreover, we provide both the consistency and the asymptotic normality for making an inference on the estimated breakpoint. Importantly, our results hold for both large and small breaks in the factor dependency structure. The estimation precision is further illustrated via a simulation study. Finally, we present two empirical applications in modeling the dynamics of the minimum wage policy in China and analyzing a limit order book dataset. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11317805
title: High-Frequency Lead-Lag Effects and Cross-Asset Linkages: A Multi-Asset Lagged Adjustment Model

description: Motivated by the empirical evidence of high-frequency lead-lag effects and cross-asset linkages, we introduce a multi-asset price formation model which generalizes standard univariate microstructure models of lagged price adjustment. Econometric inference on such model provides: (i) a unified statistical test for the presence of lead-lag correlations in the latent price process and for the existence of a multi-asset price formation mechanism; (ii) separate estimation of contemporaneous and lagged dependencies; (iii) an unbiased estimator of the integrated covariance of the efficient martingale price process that is robust to microstructure noise, asynchronous trading, and lead-lag dependencies. Through an extensive simulation study, we compare the proposed estimator to alternative approaches and show its advantages in recovering the true lead-lag structure of the latent price process. Our application to a set of NYSE stocks provides empirical evidence for the existence of a multi-asset price formation mechanism and sheds light on its market microstructure determinants. Supplementary materials for this article are available online.