Publication Type
Journal Article
Version
publishedVersion
Publication Date
7-2012
Abstract
In this paper we consider the problem of estimating semiparametric panel data models with cross section dependence, where the individual-specific regressors enter the model nonparametrically whereas the common factors enter the model linearly. We consider both heterogeneous and homogeneous regression relationships when both the time and cross-section dimensions are large. We propose sieve estimators for the nonparametric regression functions by extending Pesaran’s (2006) common correlated effect (CCE) estimator to our semiparametric framework. Asymptotic normal distributions for the proposed estimators are derived and asymptotic variance estimators are provided. Monte Carlo simulations indicate that our estimators perform well in finite samples.
Keywords
Common factor, Cross-section dependence, Heterogeneous regression, Panel data, Sieve estimation
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
169
Issue
1
First Page
34
Last Page
47
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2012.01.006
Publisher
Elsevier
Citation
SU, Liangjun and JIN, Sainan.
Sieve Estimation of Panel Data Models with Cross Section Dependence. (2012). Journal of Econometrics. 169, (1), 34-47.
Available at: https://ink.library.smu.edu.sg/soe_research/1337
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2012.01.006