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ASYMPTOTIC THEORY IN FIXED EFFECTS PANEL DATA SEEMINGLY UNRELATED PARTIALLY LINEAR REGRESSION MODELS

Published online by Cambridge University Press:  13 December 2013

Jinhong You  and
Xian Zhou
Jinhong You*
Affiliation:
Shanghai University of Finance and Economics and Key Laboratory of Mathematical Economics
Xian Zhou
Affiliation:
Macquarie University
*
*Address correspondence to Jinhong You, School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai 200433, P. R. China. e-mail: [email protected].
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Abstract

This paper deals with statistical inference for the fixed effects panel data seemingly unrelated partially linear regression model. The model naturally extends the traditional fixed effects panel data regression model to allow for semiparametric effects. Multiple regression equations are permitted, and the model includes the aggregated partially linear model as a special case. A weighted profile least squares estimator for the parametric components is proposed and shown to be asymptotically more efficient than those neglecting the contemporaneous correlation. Furthermore, a weighted two-stage estimator for the nonparametric components is also devised and shown to be asymptotically more efficient than those based on individual regression equations. The asymptotic normality is established for estimators of both parametric and nonparametric components. The finite-sample performance of the proposed methods is evaluated by simulation studies.

Type
ARTICLES
Information
Econometric Theory , Volume 30 , Issue 2 , April 2014 , pp. 407 - 435
Copyright
Copyright © Cambridge University Press 2013 

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