Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions
AbstractIn 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 nite sample behavior of the proposed estimation method.
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Bibliographic InfoPaper provided by University of Adelaide, School of Economics in its series School of Economics Working Papers with number 2010-09.
Length: 31 pages
Date of creation: May 2010
Date of revision:
asymptotic distribution; local linear smoother; minimum average variance estimation; panel data; semiparametric estimation; single-index models;
Other versions of this item:
- Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions," Econometric Reviews, Taylor & Francis Journals, vol. 32(8), pages 928-955, November.
- Jia Chen & Jiti Gao & Degui Li, 2011. "Estimation in Single-Index Panel Data Models with Heterogeneous Link Functions," Monash Econometrics and Business Statistics Working Papers 12/11, Monash University, Department of Econometrics and Business Statistics.
- C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
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