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NONPARAMETRIC ESTIMATION OF GENERALIZED TRANSFORMATION MODELS WITH FIXED EFFECTS

Published online by Cambridge University Press:  20 January 2022

Songnian Chen ,
Xun Lu  and
Xi Wang
Songnian Chen*
Affiliation:
Hong Kong University of Science and Technology
Xun Lu
Affiliation:
The Chinese University of Hong Kong
Xi Wang
Affiliation:
Jinan University
*
Address correspondence to Songnian Chen, Department of Economics, Hong Kong University of Science and Technology, Kowloon, Hong Kong; e-mail: [email protected].
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Abstract

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This paper considers a generalized panel data transformation model with fixed effects where the structural function is assumed to be additive. In our model, no parametric assumptions are imposed on the transformation function, the structural function, or the distribution of the idiosyncratic error term. The model is widely applicable and includes many popular panel data models as special cases. We propose a kernel-based nonparametric estimator for the structural function. The estimator has a closed-form solution and is easy to implement. We study the asymptotic properties of our estimator and show that it is asymptotically normally distributed. The Monte Carlo simulations demonstrate that our new estimator performs well in finite samples.

Type
ARTICLES
Information
Econometric Theory , Volume 39 , Issue 2 , April 2023 , pp. 357 - 388
Copyright
© The Author(s), 2022. Published by Cambridge University Press

Footnotes

We would like to thank the Editor, Peter C.B. Phillips, a Co-Editor, and two anonymous referees for their many constructive comments. We also thank Songxi Chen, Yingyao Hu, Whitney Newey, and the conference and seminar participants at the CEMP Econometric Workshop, 2019 Guangzhou Econometrics Workshop, and Peking University for their helpful comments. Lu acknowledges support from the Hong Kong Research Grants Council (RGC) under grant number 16504819, National Natural Science Foundation of China (NSFC) under grant number 72133002, and the Chinese University of Hong Kong for the start-up fund. Wang gratefully acknowledges the support by the National Natural Science Foundation of China Grant through NSFC7190312.

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