Jushan Bai
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View article: Taxonomy and Estimation of Multiple Breakpoints in High-Dimensional Factor Models
Taxonomy and Estimation of Multiple Breakpoints in High-Dimensional Factor Models Open
This paper investigates the estimation of high-dimensional factor models in which factor loadings undergo an unknown number of structural changes over time. Given that a model with multiple changes in factor loadings can be observationally…
View article: Bayesian inference for dynamic spatial quantile models with interactive effects
Bayesian inference for dynamic spatial quantile models with interactive effects Open
With the rapid advancement of information technology and data collection systems, large-scale spatial panel data presents new methodological and computational challenges. This paper introduces a dynamic spatial panel quantile model that in…
View article: Bayesian inference for dynamic spatial quantile models with interactive effects
Bayesian inference for dynamic spatial quantile models with interactive effects Open
View article: Bayesian inference for dynamic spatial quantile models with interactive effects *
Bayesian inference for dynamic spatial quantile models with interactive effects * Open
View article: Likelihood approach to dynamic panel models with interactive effects
Likelihood approach to dynamic panel models with interactive effects Open
This paper studies dynamic panel models with a factor error structure that is correlated with the regressors. Both short panels (small T) and long panels (large T) are considered. A dynamic panel forms a simultaneous-equation system, and u…
View article: Causal Inference Using Factor Models
Causal Inference Using Factor Models Open
View article: Efficiency of QMLE for dynamic panel data models with interactive effects
Efficiency of QMLE for dynamic panel data models with interactive effects Open
This paper studies the problem of efficient estimation of panel data models in the presence of an increasing number of incidental parameters. We formulate the dynamic panel as a simultaneous equations system, and derive the efficiency boun…
View article: Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity
Large-Scale Generalized Linear Models for Longitudinal Data with Grouped Patterns of Unobserved Heterogeneity Open
This article provides methods for flexibly capturing unobservable heterogeneity from longitudinal data in the context of an exponential family of distributions. The group memberships of individual units are left unspecified, and their hete…
View article: Likelihood ratio test for structural changes in factor models
Likelihood ratio test for structural changes in factor models Open
A factor model with a break in its factor loadings is observationally equivalent to a model without changes in the loadings but a change in the variance of its factors. This effectively transforms a structural change problem of high dimens…
View article: Factor-based imputation of missing values and covariances in panel data of large dimensions
Factor-based imputation of missing values and covariances in panel data of large dimensions Open
View article: Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity
Large-scale generalized linear longitudinal data models with grouped patterns of unobserved heterogeneity Open
View article: Approximate Factor Models with Weaker Loadings
Approximate Factor Models with Weaker Loadings Open
Pervasive cross-section dependence is increasingly recognized as a characteristic of economic data and the approximate factor model provides a useful framework for analysis. Assuming a strong factor structure where $\Lop\Lo/N^α$ is positiv…
View article: Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data
Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data Open
This paper proposes an imputation procedure that uses the factors estimated from a tall block along with the re-rotated loadings estimated from a wide block to impute missing values in a panel of data. Assuming that a strong factor structu…
View article: Factor-Based Imputation of Missing Values and Covariances in Panel Data\n of Large Dimensions
Factor-Based Imputation of Missing Values and Covariances in Panel Data\n of Large Dimensions Open
Economists are blessed with a wealth of data for analysis, but more often\nthan not, values in some entries of the data matrix are missing. Various\nmethods have been proposed to handle missing observations in a few variables.\nWe exploit …
View article: Quasi-maximum likelihood estimation of break point in high-dimensional factor models
Quasi-maximum likelihood estimation of break point in high-dimensional factor models Open
This paper estimates the break point for large-dimensional factor models with a single structural break in factor loadings at a common unknown date. First, we propose a quasi-maximum likelihood (QML) estimator of the change point based on …
View article: Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity
Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity Open
This article introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobserva…
View article: Simpler Proofs for Approximate Factor Models of Large Dimensions
Simpler Proofs for Approximate Factor Models of Large Dimensions Open
Estimates of the approximate factor model are increasingly used in empirical work. Their theoretical properties, studied some twenty years ago, also laid the ground work for analysis on large dimensional panel data models with cross-sectio…
View article: Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations
Feasible Generalized Least Squares for Panel Data with Cross-sectional and Serial Correlations Open
This paper considers generalized least squares (GLS) estimation for linear panel data models. By estimating the large error covariance matrix consistently, the proposed feasible GLS (FGLS) estimator is more efficient than the ordinary leas…
View article: Standard Errors for Panel Data Models with Unknown Clusters
Standard Errors for Panel Data Models with Unknown Clusters Open
This paper develops a new standard-error estimator for linear panel data models. The proposed estimator is robust to heteroskedasticity, serial correlation, and cross-sectional correlation of unknown forms. The serial correlation is contro…
View article: Robust Principal Component Analysis with Non-Sparse Errors
Robust Principal Component Analysis with Non-Sparse Errors Open
We show that when a high-dimensional data matrix is the sum of a low-rank matrix and a random error matrix with independent entries, the low-rank component can be consistently estimated by solving a convex minimization problem. We develop …
View article: A quantile-based asset pricing model
A quantile-based asset pricing model Open
It is well-known that the standard estimators of the risk premium in asset pricing models are biased when some price factors are omitted. To address this problem, we propose a novel quantile-based asset pricing model and a new estimation m…
View article: Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity
Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity Open
This article introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobserva…
View article: Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity
Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity Open
This article introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobserva…
View article: Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity
Quantile co-movement in financial markets: A panel quantile model with unobserved heterogeneity Open
This paper introduces a new procedure for analyzing the quantile co-movement of a large number of financial time series based on a large-scale panel data model with factor structures. The proposed method attempts to capture the unobservabl…
View article: Principal Components and Regularized Estimation of Factor Models
Principal Components and Regularized Estimation of Factor Models Open
It is known that the common factors in a large panel of data can be consistently estimated by the method of principal components, and principal components can be constructed by iterative least squares regressions. Replacing least squares w…
View article: Quantile Co-Movement in Financial Markets; a Panel Quantile Model with Unobserved Heterogeneity
Quantile Co-Movement in Financial Markets; a Panel Quantile Model with Unobserved Heterogeneity Open
View article: Estimation and Inference of Change Points in High Dimensional Factor Models
Estimation and Inference of Change Points in High Dimensional Factor Models Open
View article: Econometric Analysis of Large Factor Models
Econometric Analysis of Large Factor Models Open
Large factor models use a few latent factors to characterize the co-movement of economic variables in a high-dimensional data set. High dimensionality brings challenges as well as new insights into the advancement of econometric theory. Be…
View article: Special Issue on Big Data
Special Issue on Big Data Open
Information and technology have revolutionized data collection. Millions of surveillance video cameras and billions of Internet searches and social media chats and tweets that produce massive data ...
View article: Estimation and Inference of Structural Changes in High Dimensional Factor Models
Estimation and Inference of Structural Changes in High Dimensional Factor Models Open