GMM Estimation for High-Dimensional Panel Data Models

Tingting Cheng (), Chaohua Dong (), Jiti Gao and Oliver Linton

No 11/22, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we study a class of high dimensional moment restriction panel data models with interactive effects, where factors are unobserved and factor loadings are nonparametrically unknown smooth functions of individual characteristics variables. We allow the dimension of the parameter vector and the number of moment conditions to diverge with sample size. This is a very general framework and includes many existing linear and nonlinear panel data models as special cases. In order to estimate the unknown parameters, factors and factor loadings, we propose a sieve-based generalized method of moments estimation method and we show that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further we establish asymptotic distributions of the proposed estimators. In addition, we propose tests for over-identification, specification of factor loading functions, and establish their large sample properties. Moreover, a number of simulation studies are conducted to examine the performance of the proposed estimators and test statistics in finite samples. An empirical example on stock return prediction is studied to demonstrate the usefulness of the proposed framework and corresponding estimation methods and testing procedures.

Keywords: Generalized method of moments; high dimensional moment model; interactive effect; over-identification issue; panel data; sieve method (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 (search for similar items in EconPapers)
Pages: 58
Date: 2022
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: GMM estimation for high-dimensional panel data models (2024)
Working Paper: GMM Estimation for High-Dimensional Panel Data Models (2022)
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