A0623
Title: GMM estimation for high-dimensional panel data models
Authors: Tingting Cheng - Nankai University (China) [presenting]
Abstract: A class of high dimensional moment restriction panel data models with interactive effects are studied, where factors are unobserved, and factor loadings are nonparametrically unknown smooth functions of individual characteristics variables. The dimension of the parameter vector and the number of moment conditions is allowed to diverge with the sample size. This general framework includes many existing linear and nonlinear panel data models as special cases. A sieve-based generalized method of the moments estimation method is proposed to estimate the unknown parameters, factors and factor loadings. It is shown that all those unknown quantities can be consistently estimated under a set of simple identification conditions. Further, asymptotic distributions of the proposed estimators are established. In addition, tests for over-identification and specification of factor loading functions are proposed, and their large sample properties are established. 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 of stock return prediction is studied to demonstrate the usefulness of the proposed framework and corresponding estimation methods and testing procedures.
