Robust Inference for High Dimensional Panel Data Models

Jiti Gao, Bin Peng () and Yayi Yan

No 9/24, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we propose a robust estimation and inferential method for high dimensional panel data models. Specifically, (1) we investigate the case where the number of regressors can grow faster than the sample size, (2) we pay particular attention to non-Gaussian, serially and cross-sectionally correlated and heteroskedastic error processes, and (3) we develop an estimation method for high-dimensional long-run covariance matrix using a thresholded estimator. Methodologically and technically, we develop two Nagaev-types of concentration inequalities: one for a partial sum and the other for a quadratic form, subject to a set of easily verifiable conditions. Leveraging these two inequalities, we also derive a non-asymptotic bound for the LASSO estimator, achieve asymptotic normality via the node-wise LASSO regression, and establish a sharp convergence rate for the thresholded heteroskedasticity and autocorrelation consistent (HAC) estimator. Our study thus provides the relevant literature with a complete toolkit for conducting inference about the parameters of interest involved in a high-dimensional panel data framework. We also demonstrate the practical relevance of these theoretical results by investigating a high-dimensional panel data model with interactive fixed effects. Moreover, we conduct extensive numerical studies using simulated and real data examples.

Keywords: Asset Pricing; Concentration Inequality; Heavy-Tailed Distribution; High Dimensional Long-Run Covariance Matrix; Thresholding (search for similar items in EconPapers)
JEL-codes: C14 C45 G12 (search for similar items in EconPapers)
Pages: 74
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.monash.edu/business/ebs/research/publications/ebs/2023/wp09-2024.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:msh:ebswps:2024-9

Ordering information: This working paper can be ordered from
http://business.mona ... -business-statistics

Access Statistics for this paper

More papers in Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics PO Box 11E, Monash University, Victoria 3800, Australia. Contact information at EDIRC.
Bibliographic data for series maintained by Professor Xibin Zhang ().