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TWO-STEP ESTIMATION OF QUANTILE PANEL DATA MODELS WITH INTERACTIVE FIXED EFFECTS

Published online by Cambridge University Press:  18 August 2022

Liang Chen
Liang Chen*
Affiliation:
Peking University HSBC Business School
*
Address correspondence to Liang Chen, Peking University HSBC Business School, No. 2199 Lishui Road, Shenzhen, Guangdong 518055, China; e-mail: [email protected].
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Abstract

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This paper considers the estimation of panel data models with interactive fixed effects where the idiosyncratic errors are subject to conditional quantile restrictions. An easy-to-implement two-step estimator is proposed for the coefficients of the observed regressors. In the first step, the principal component analysis is applied to the cross-sectional averages of the regressors to estimate the latent factors. In the second step, the smoothed quantile regression is used to estimate the coefficients of the observed regressors and the factor loadings jointly. The consistency and asymptotic normality of the estimator are established under large $N,T$ asymptotics. It is found that the asymptotic distribution of the estimator suffers from asymptotic biases, and this paper shows how to correct the biases using both analytical and split-panel jackknife bias corrections. Simulation studies confirm that the proposed estimator performs well with moderate sample sizes.

Type
ARTICLES
Information
Econometric Theory , Volume 40 , Issue 2 , April 2024 , pp. 419 - 446
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

I am indebted to Liangjun Su and three anonymous referees for their constructive inputs which have greatly improved the paper. Financial support from the National Natural Science Foundation of China (Grant No. 71703089) is gratefully acknowledged.

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