About

Jushan Bai is a professor in the Department of Economics at Columbia University. He holds a Ph.D. from the University of California at Berkeley. His research focuses on econometrics, with particular emphasis on the development and application of statistical methods for economic data analysis. Bai's work contributes to the understanding of econometric models and their use in empirical economic research, although specific details of his research contributions are not provided on the page.

Research topics

• Statistics
• Econometrics
• Mathematics
• Applied mathematics
• Mathematical optimization

Selected publications

• Efficiency of QMLE for Dynamic Panel Data Models with Interactive Effects

  Journal of the American Statistical Association · 2025-09-05 · 1 citations

  article1st authorCorresponding
  PublisherDOI

• Taxonomy and Estimation of Multiple Breakpoints in High-Dimensional Factor Models

  arXiv (Cornell University) · 2025-03-09

  preprintOpen access

  This paper proposes a quasi-maximum likelihood (QML) estimator for break points in high-dimensional factor models, specifically accounting for multiple structural breaks. We begin by establishing a necessary and sufficient condition to categorize two distinct types of breaks in factor loadings: singular changes and rotational changes. The analysis of the nearly singular subsample covariance matrices of the pseudo-factors plays a key role in our approach. It allows us to demonstrate that the QML estimator precisely identifies the true breakpoint with probability tending to one for singular changes. For rotational changes, we demonstrate that the estimator exhibits stochastically bounded estimation errors, implying break fraction consistency. Furthermore, we introduce an information criterion to estimate the number of breaks, proving that it can detect the true number with probability tending to one. Monte Carlo simulations confirm the strong finite sample performance of our proposed methods. Finally, we provide an empirical example to estimate structural breakpoints in the FRED-MD dataset spanning 1959 to 2024.

  PublisherOA PDFDOI

• Bayesian inference for dynamic spatial quantile models with interactive effects

  ArXiv.org · 2025-03-02

  preprintOpen access

  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 incorporates unobserved heterogeneity. The proposed model captures the dynamic structure of panel data, high-dimensional cross-sectional dependence, and allows for heterogeneous regression coefficients. To estimate the model, we propose a novel Bayesian Markov Chain Monte Carlo (MCMC) algorithm. Contributions to Bayesian computation include the development of quantile randomization, a new Gibbs sampler for structural parameters, and stabilization of the tail behavior of the inverse Gaussian random generator. We establish Bayesian consistency for the proposed estimation method as both the time and cross-sectional dimensions of the panel approach infinity. Monte Carlo simulations demonstrate the effectiveness of the method. Finally, we illustrate the applicability of the approach through a case study on the quantile co-movement structure of the gasoline market.

  PublisherOA PDFDOI

• Bayesian inference for dynamic spatial quantile models with interactive effects

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Bayesian inference for dynamic spatial quantile models with interactive effects *

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Likelihood approach to dynamic panel models with interactive effects

  Journal of Econometrics · 2024-01-06 · 5 citations

  articleOpen access1st authorCorresponding

  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 under the factor error structure, there exist constraints between the mean and the covariance matrix. We explore the constraints through a quasi-FIML (full information maximum likelihood) approach. The quasi-FIML approach does not estimate individual effects, even if they are fixed constants, thus circumventing the incidental parameters problem in the cross-sectional dimension. The factor process is treated as parameters and it can have arbitrary dynamics. We show that there is no incidental parameters bias, for fixed or large T, and that the estimator is centered at zero even when scaled by the fast convergence rate of root-NT. We also study the efficiency of the quasi-FIML estimator. Finally, we develop a feasible and fast algorithm for computing the quasi-FIML estimators under interactive effects.

  PublisherDOI

• Reprint of: The likelihood ratio test for structural changes in factor models

  Journal of Econometrics · 2024-05-09

  article1st author
  PublisherDOI

• Scenario-based quantile connectedness of the U.S. interbank liquidity risk network

  Journal of Econometrics · 2024-06-19 · 8 citations

  article
  PublisherDOI

• Causal Inference Using Factor Models

  SSRN Electronic Journal · 2024-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• The likelihood ratio test for structural changes in factor models

  Journal of Econometrics · 2024-01-01 · 14 citations

  article1st author
  PublisherDOI

Recent grants

• Topics in Dynamic Panel Data Analysis, Time-Varying Individual Heterogeneities, and Cross-Sectional Dependence

  NSF · $227k · 2010–2014

• Structural Changes in High Dimensional Factor Models

  NSF · $247k · 2017–2020

• New Approaches for Dynamic Panel Data Analysis

  NSF · $238k · 2014–2017

Frequent coauthors

• Serena Ng

  Columbia University

  54 shared

• Tomohiro Ando

  Gifu University

  31 shared

• Kunpeng Li

  Yellow River Institute of Hydraulic Research

  29 shared

• Peng Wang

  20 shared

• Yuan Liao

  19 shared

• Chihwa Kao

  University of Connecticut

  9 shared

• Pierre Perrón

  Boston University

  9 shared

• Xu Han

  Institute of Psychology, Chinese Academy of Sciences

  9 shared

Labs

• Jushan BaiPI

Awards & honors

• 2020 Annual Wueller Stipend Winners Announced
• Romine Senior Seminar Prize Recipients
• Sanford S. Parker Prize Recipients