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Indirect Inference for Nonlinear Panel Models with Fixed Effects

Shuowen Chen

Published 2022 in Unknown venue

ABSTRACT

Fixed eect estimators of nonlinear panel data models suer from the incidental parameter problem. This leads to two undesirable consequences in applied research: (1) point estimates are subject to large biases, and (2) condence intervals have incorrect coverages. This paper proposes a simulation–based method for bias reduction. The method simulates data using the model with estimated individual eects, and nds values of parameters by equating xed eect estimates obtained from observed and simulated data. The asymptotic framework provides consistency, bias correction, and asymptotic normality results. An application and simulations to female labor force participation illustrates the nite–sample performance of the method. this project. I am indebted to Iván Fernández-Val, Jean–Jacques Forneron and Hiroaki Kaido for patience, guidance and encouragement. For helpful discussions and suggestions, I thank Aureo de Paula, Karun Adusumilli, Jiaying Gu, Eric Hardy, Dennis Kristensen, Yan Liu, Xun Lu, Pierre Perron, Zhongjun Qu, Pascual Restrepo, Marc Rysman, Xiaoxia Shi, Guang Zhang, Beixi Zhou and participants in numerous seminars, reading groups and job interviews. All errors are mine.

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