A0371
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 is studied with interactive effects, 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 are allowed 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, a sieve-based generalized method, as well as the moments estimation method, is proposed. It is shown that under a set of simple identification conditions, all those unknown quantities can be consistently estimated. Further, asymptotic distributions of the proposed estimators are established. In addition, tests for over-identification are proposed, factor loading functions are specified, and 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.
