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Abstract Details

Activity Number: 472
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract - #305130
Title: Nonparametric Panel Data Model Estimation: Efficiency Comparison of Kernel and Spline Estimators
Author(s): Yue Liu*+
Companies: University of California at Riverside
Address: 1109 Olmsted Hall, Riverside, CA, 92521,
Keywords: Nonparametrics ; Panel Data Model ; Fixed Effects ; Random Effects ; Kernel Regression ; Splines

In our paper we discuss the problem of estimating nonparametric panel data models with fixed effects as well as with random effects. Two methods of estimating the nonparametric function and its derivative can be considered: kernel methods and spline methods. Seven estimators proposed in recent statistical and econometric literatures, five from kernel methods and two from spline methods, are compared in this paper in terms of their statistical efficiency and computational efficiency. Monte Carlo simulations and empirical data are both involved in the comparison.

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