Confidence intervals of treatment effects in panel data models with interactive fixed effects

Xingyu Li, Yan Shen and Qiankun Zhou

Journal of Econometrics, 2024, vol. 240, issue 1

Abstract: We augment the factor-based estimation of treatment effects proposed by Bai and Ng (2021) with easy-to-implement and nonparametric confidence intervals of treatment effects on every treated unit at every post-treatment time. The construction of confidence intervals entails a residual-based bootstrap resampling procedure. This method does not rely on any parametric assumption on the distribution of idiosyncratic errors and it is robust to weak cross-sectional and time-series dependence among idiosyncratic errors. We prove the asymptotic validity of the proposed confidence intervals as the numbers of control units and pre-treatment times go to infinity. We also extend this method to cases where the common factors and covariates (if any) are unit root processes. Monte Carlo experiments show that the proposed confidence intervals are well-behaved in finite samples and outperform confidence intervals based on normal quantiles. Empirical applications with two classical datasets add informative confidence intervals to existing point estimates of treatment effects.

Keywords: Bootstrap; Confidence interval; Treatment effects; Panel data analysis; Interactive fixed effects; Nonstationarity (search for similar items in EconPapers)
JEL-codes: C01 C21 C31 I18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407624000307
Full text for ScienceDirect subscribers only

Related works:
Working Paper: Confidence Intervals of Treatment Effects in Panel Data Models with Interactive Fixed Effects (2022)
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:eee:econom:v:240:y:2024:i:1:s0304407624000307

DOI: 10.1016/j.jeconom.2024.105684

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().