Asymptotic theory in fixed effects panel data seemingly unrelated partially linear regression models

Jinhong You*, Xian Zhou

*Corresponding author for this work

Research output: Contribution to journalArticle

6 Citations (Scopus)

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

Original languageEnglish
Pages (from-to)407-435
Number of pages29
JournalEconometric Theory
Volume30
Issue number2
DOIs
Publication statusPublished - Apr 2014

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