High-Dimensional Panel Data with Time Heterogeneity: Estimation and Inference

92 Pages Posted: 26 Sep 2015 Last revised: 18 Jan 2017

Yinchu Zhu

University of Oregon

Date Written: January 15, 2017

Abstract

We consider high-dimensional panel data models (large cross sections and long time horizons) with interactive fixed effects and allow the covariate/slope coefficients to vary over time without any restrictions. The parameter of interest is the vector that contains all the covariate effects across time. This vector has dimensionality tending to infinity, potentially much faster than the cross-sectional sample size. We develop methods for the estimation and inference of this high-dimensional vector, i.e., the entire trajectory of time variation in covariate effects. We show that both the consistency of our estimator and the asymptotic accuracy of the proposed inference procedure hold uniformly in time. Our methodology can be applied to several important issues in econometrics, such as constructing confidence bands for the entire path of covariate coefficients across time, testing the time-invariance of slope coefficients and estimation and inference of patterns of time variations, including structural breaks and regime switching. An important feature of our method is that it provides inference procedures for the time variation in prespecified components of slope coefficients while allowing for arbitrary time variation in other components. Computationally, our procedures do not require any numerical optimization and are very simple to implement. Monte Carlo simulations demonstrate favorable properties of our methods in finite samples. We illustrate our methods through empirical applications in finance and economics.

Keywords: Inference in high dimensions, time-heterogeneity, interactive fixed effects

JEL Classification: C23, C14

Suggested Citation

Zhu, Yinchu, High-Dimensional Panel Data with Time Heterogeneity: Estimation and Inference (January 15, 2017). Available at SSRN: https://ssrn.com/abstract=2665374 or http://dx.doi.org/10.2139/ssrn.2665374

Yinchu Zhu (Contact Author)

University of Oregon ( email )

955 E. 13th Ave
Eugene, CA 97403
United States

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