Semiparametric Trending Panel Data Models with Cross-Sectional Dependence

Jia Chen (), Jiti GAO () and Degui Li

No 15/11, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonparametric time trend function. As both the time series length T and the cross-sectional size N tend to infinity simultaneously, the resulting estimator of the parameter vector is asymptotically normal with a rate of convergence of Op(NT)^{-1/2}. Meanwhile, the asymptotic distribution for the estimator of the nonparametric trend function is also established with a rate of convergence of Op(NTh)^{-1/2}. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method is applied to analyze a CPI data set as well as an input-output data set.

Keywords: Cross-sectional dependence; nonlinear time trend; panel data; profile likelihood; semiparametric regression. (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets
Date: 2011-09
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Related works:
Journal Article: Semiparametric trending panel data models with cross-sectional dependence (2012)
Working Paper: Semiparametric Trending Panel Data Models with Cross-Sectional Dependence (2010)
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