Estimation in Partially Linear Single-Index Panel Data Models with Fixed Effects

Jia Chen, Jiti Gao and Degui Li

No 14/11, Monash Econometrics and Business Statistics Working Papers from Monash University, Department of Econometrics and Business Statistics

Abstract: In this paper, we consider semiparametric estimation in a partially linear single-index panel data model with fixed effects. Without taking the difference explicitly, we propose using a semiparametric minimum average variance estimation (SMAVE) based on a dummy-variable method to remove the fixed effects and obtain consistent estimators for both the parameters and the unknown link function. As both the cross section size and the time series length tend to infinity, we not only establish an asymptotically normal distribution for the estimators of the parameters in the single index and the linear component of the model, but also obtain an asymptotically normal distribution for the nonparametric local linear estimator of the unknown link function. The asymptotically normal distributions of the proposed estimators are similar to those obtained in the random effects case. In addition, we study several partially linear single-index dynamic panel data models. The methods and results are augmented by simulation studies and illustrated by an application to a cigarette-demand data set in the US from 1963-1992

Keywords: Fixed effects; local linear smoothing; panel data; semiparametric estimation; single-index models (search for similar items in EconPapers)
JEL-codes: C13 C14 C23 (search for similar items in EconPapers)
Pages: 32 pages
Date: 2011-09
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (5)

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Journal Article: Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects (2013)
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