cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.11317805.v2
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.11387673
title: Dynamic Two Stage Modeling for Category-Level and Brand-Level Purchases Using Potential Outcome Approach With Bayes Inference

description: We propose an econometric two-stage model for category-level purchase and brand-level purchase that allows for simultaneous brand purchases in the analysis of scanner panel data. The proposed model formulation is consistent with the traditional theory of consumer behavior. We conduct Bayesian estimation with the Markov chain Monte Carlo algorithm for our proposed model. The simulation studies show that previously proposed related models can cause severe bias in predicting future brand choices, while the proposed method can effectively predict them. Additionally in a marketing application, the proposed method can examine brand switching behaviors that existing methods cannot. Moreover, we show that the prediction accuracy of the proposed method is higher than that of existing methods.
DOI: 10.6084/m9.figshare.11573277
title: Fitting Vast Dimensional Time-Varying Covariance Models

description: Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11866179.v2
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 (VAR) 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.
DOI: 10.6084/m9.figshare.11573277.v2
title: Fitting Vast Dimensional Time-Varying Covariance Models

description: Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11704236.v14
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.11871945
title: Incorporating Graphical Structure of Predictors in Sparse Quantile Regression

description: Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset.
DOI: 10.6084/m9.figshare.11871945.v3
title: Incorporating Graphical Structure of Predictors in Sparse Quantile Regression

description: Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset.
DOI: 10.6084/m9.figshare.11387673.v2
title: Dynamic Two Stage Modeling for Category-Level and Brand-Level Purchases Using Potential Outcome Approach With Bayes Inference

description: We propose an econometric two-stage model for category-level purchase and brand-level purchase that allows for simultaneous brand purchases in the analysis of scanner panel data. The proposed model formulation is consistent with the traditional theory of consumer behavior. We conduct Bayesian estimation with the Markov chain Monte Carlo algorithm for our proposed model. The simulation studies show that previously proposed related models can cause severe bias in predicting future brand choices, while the proposed method can effectively predict them. Additionally in a marketing application, the proposed method can examine brand switching behaviors that existing methods cannot. Moreover, we show that the prediction accuracy of the proposed method is higher than that of existing methods.
DOI: 10.6084/m9.figshare.11871966
title: A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments

description: I propose a generalized method of moments estimator for structural vector autoregressions with independent and non-Gaussian shocks. The shocks are identified by exploiting information contained in higher moments of the data. Extending the standard identification approach, which relies on the covariance, to the coskewness and cokurtosis allows the simultaneous interaction to be identified and estimated without any further restrictions. I analyze the finite sample properties of the estimator and apply it to illustrate the simultaneous interaction between economic activity, oil, and stock prices. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11573277.v1
title: Fitting Vast Dimensional Time-Varying Covariance Models

description: Estimation of time-varying covariances is a key input in risk management and asset allocation. ARCH-type multivariate models are used widely for this purpose. Estimation of such models is computationally costly and parameter estimates are meaningfully biased when applied to a moderately large number of assets. Here, we propose a novel estimation approach that suffers from neither of these issues, even when the number of assets is in the hundreds. The theory of this new method is developed in some detail. The performance of the proposed method is investigated using extensive simulation studies and empirical examples. Supplementary materials for this article are available online.
DOI: 10.6084/m9.figshare.11704236.v13
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.11591829
title: Inducing Sparsity and Shrinkage in Time-Varying Parameter Models

description: Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce this uncertainty and improve forecasts. In this article, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise, we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
DOI: 10.6084/m9.figshare.11871945.v2
title: Incorporating Graphical Structure of Predictors in Sparse Quantile Regression

description: Quantile regression in high-dimensional settings is useful in analyzing high-dimensional heterogeneous data. In this article, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection, and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset.
DOI: 10.6084/m9.figshare.11591829.v2
title: Inducing Sparsity and Shrinkage in Time-Varying Parameter Models

description: Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce this uncertainty and improve forecasts. In this article, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise, we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
DOI: 10.6084/m9.figshare.11871966.v1
title: A Generalized Method of Moments Estimator for Structural Vector Autoregressions Based on Higher Moments

description: I propose a generalized method of moments estimator for structural vector autoregressions with independent and non-Gaussian shocks. The shocks are identified by exploiting information contained in higher moments of the data. Extending the standard identification approach, which relies on the covariance, to the coskewness and cokurtosis allows the simultaneous interaction to be identified and estimated without any further restrictions. I analyze the finite sample properties of the estimator and apply it to illustrate the simultaneous interaction between economic activity, oil, and stock prices.
DOI: 10.6084/m9.figshare.11926275
title: Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes

description: This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy (1) has zero distributional (average) effect for all subpopulations defined by covariate values, and (2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan–Meier integrals and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation but can also account for treatment noncompliance—an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric n−1/2-rate, <i>n</i> being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.
DOI: 10.6084/m9.figshare.11926275.v2
title: Nonparametric Tests for Treatment Effect Heterogeneity With Duration Outcomes

description: This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy (1) has zero distributional (average) effect for all subpopulations defined by covariate values, and (2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan–Meier integrals and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation but can also account for treatment noncompliance—an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric n−1/2-rate, <i>n</i> being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.
DOI: 10.6084/m9.figshare.11591829.v1
title: Inducing Sparsity and Shrinkage in Time-Varying Parameter Models

description: Time-varying parameter (TVP) models have the potential to be over-parameterized, particularly when the number of variables in the model is large. Global-local priors are increasingly used to induce shrinkage in such models. But the estimates produced by these priors can still have appreciable uncertainty. Sparsification has the potential to reduce this uncertainty and improve forecasts. In this article, we develop computationally simple methods which both shrink and sparsify TVP models. In a simulated data exercise, we show the benefits of our shrink-then-sparsify approach in a variety of sparse and dense TVP regressions. In a macroeconomic forecasting exercise, we find our approach to substantially improve forecast performance relative to shrinkage alone.
DOI: 10.6084/m9.figshare.11926275.v1
title: Nonparametric Tests for Treatment Effect Heterogeneity with Duration Outcomes

description: This article proposes different tests for treatment effect heterogeneity when the outcome of interest, typically a duration variable, may be right-censored. The proposed tests study whether a policy 1) has zero distributional (average) effect for all subpopulations defined by covariate values, and 2) has homogeneous average effect across different subpopulations. The proposed tests are based on two-step Kaplan-Meier integrals and do not rely on parametric distributional assumptions, shape restrictions, or on restricting the potential treatment effect heterogeneity across different subpopulations. Our framework is suitable not only to exogenous treatment allocation but can also account for treatment noncompliance - an important feature in many applications. The proposed tests are consistent against fixed alternatives, and can detect nonparametric alternatives converging to the null at the parametric n−1/2-rate, n being the sample size. Critical values are computed with the assistance of a multiplier bootstrap. The finite sample properties of the proposed tests are examined by means of a Monte Carlo study and an application about the effect of labor market programs on unemployment duration. Open-source software is available for implementing all proposed tests.
DOI: 10.6084/m9.figshare.13480302.v1
title: Fast Markov chain Monte Carlo for high dimensional Bayesian regression models with shrinkage priors

description: In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, <i>p</i>, is large. Computation of the intractable posterior is often done with three-block Gibbs samplers (3BG ), based on representing the shrinkage priors as scale mixtures of Normal distributions. An alternative computing tool is a state of the art Hamiltonian Monte Carlo (HMC) method, which can be easily implemented in the Stan software. However, we found both existing methods to be inefficient and often impractical for large <i>p</i> problems. Following the general idea of Rajaratnam et al. (2018), we propose two-block Gibbs samplers (2BG ) for three commonly used shrinkage models, namely, the Bayesian group lasso, the Bayesian sparse group lasso and the Bayesian fused lasso models. We demonstrate with simulated and real data examples that the Markov chains underlying 2BG ’s converge much faster than that of 3BG ’s, and no worse than that of HMC. At the same time, the computing costs of 2BG ’s per iteration are as low as that of 3BG ’s, and can be several orders of magnitude lower than that of HMC. As a result, the newly proposed 2BG is the only practical computing solution to do Bayesian shrinkage analysis for datasets with large <i>p</i>. Further, we provide theoretical justifications for the superior performance of 2BG ’s. We establish geometric ergodicity (GE) of Markov chains associated with the 2BG for each of the three Bayesian shrinkage models. We also prove, for most cases of the Bayesian group lasso and the Bayesian sparse group lasso model, the Markov operators for the 2BG chains are trace-class. Whereas for all cases of all three Bayesian shrinkage models, the Markov operator for the 3BG chains are not even Hilbert-Schmidt.