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Estimating Quantile Treatment Effects for Panel Data

Zongwu Cai, Ying Fang, Ming Lin and Mingfeng Zhan
Additional contact information
Zongwu Cai: Department of Economics, The University of Kansas, Lawrence, KS 66045, USA
Ying Fang: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics & Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China
Ming Lin: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China and Department of Statistics and Data Science, School of Economics, Xiamen University, Xiamen, Fujian 361005, China
Mingfeng Zhan: The Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, Fujian 361005, China

No 202205, WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS from University of Kansas, Department of Economics

Abstract: Motivated by the paper by Hsiao, Ching and Wan (2012), which proposed a factor-based model to estimate the average treatment effect with panel data, this paper proposes a quantile treatment effect model for panel data to characterize the distributional effect of a treatment. We propose to estimate the counterfactual quantile for the treated unit by using the relationship between conditional and unconditional distributions. Also, the asymptotic properties for the proposed quantile treatment effect estimator are established, together with discussing the choice of control units and covariates. A simulation study is conducted to illustrate our method. Finally, the proposed method is applied to estimate the quantile treatment effects of introducing CSI 300 index futures trading on both the log-return and volatility of the stock market in China.

Keywords: LASSO method; Panel data; Nonparametric estimation; Quantile regression; Treatment effects. (search for similar items in EconPapers)
JEL-codes: C13 C14 C33 C52 C54 (search for similar items in EconPapers)
Date: 2022-02, Revised 2022-02
New Economics Papers: this item is included in nep-cna, nep-ecm and nep-his
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