LOCAL GMM ESTIMATION OF SEMIPARAMETRIC PANEL DATA WITH SMOOTH COEFFICIENT MODELS

被引：21
|
作者：
Tran, Kien C. [1 ]
Tsionas, Efthymios G. [2 ]
机构：
[1] Univ Lethbridge, Dept Econ, Lethbridge, AB T1K 3M4, Canada
[2] Athens Univ Econ & Business, Dept Econ, Athens, Greece
来源：
ECONOMETRIC REVIEWS | 2010年 / 29卷 / 01期
关键词：
Local Generalized Method of Moments; Monte Carlo simulation; Semiparametric panel data model; Smooth coefficient; EFFICIENT ESTIMATION; GENERALIZED-METHOD; REGRESSION-MODELS;
D O I：
10.1080/07474930903327856
中图分类号：
F [经济];
学科分类号：
02 ;
摘要：
In this article, we consider the estimation of semiparametric panel data smooth coefficient models. We propose a class of local generalized method of moments (LGMM) estimators that are simple and easy to implement in practice. We show that the proposed LGMM estimators are consistent and asymptotically normal. Monte Carlo simulations suggest that our proposed estimator performs quite well in finite samples. An empirical application using a large panel of U. K. firms is also presented.
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页码：39 / 61
页数：23
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