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

出版	Department of Econometrics and Business Statistics, Monash Univ., 2010
URL	http://books.google.com.hk/books?id=Vs1WAQAACAAJ&hl=&source=gbs_api

註釋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.