Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions

Content Provider	Semantic Scholar
Author	Chen, Jia Gao, Jiti Li, Degui
Copyright Year	2010
Abstract	In this paper, we study semiparametric estimation for a single–index panel data model where the nonlinear link function varies among the individuals. We propose using the so–called refined minimum average variance estimation based on a local linear smoothing method to estimate both the parameters in the single–index and the average link function. As the cross–section dimension N and the time series dimension T tend to infinity simultaneously, we establish asymptotic distributions for the proposed parametric and nonparametric estimates. In addition, we provide two real–data examples to illustrate the finite sample behavior of the proposed estimation method in this paper.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2011/wp12-11.pdf
Alternate Webpage(s)	http://www.economics.adelaide.edu.au/research/papers/doc/wp2010-09.pdf
Alternate Webpage(s)	http://www.monash.edu/business/econometrics-and-business-statistics/research/publications/ebs/wp12-11.pdf
Alternate Webpage(s)	http://economics.adelaide.edu.au/research/papers/doc/wp2010-09.pdf
Alternate Webpage(s)	https://economics.adelaide.edu.au/research/papers/doc/wp2010-09.pdf
Language	English
Access Restriction	Open
Subject Keyword	Data model Estimated Generalized linear model Nonlinear system Panel data Sample Variance Semiparametric model Smoothing (statistical technique) Time series
Content Type	Text
Resource Type	Article