Estimation of a nonlinear panel data model with semiparametric individual effects. (English) Zbl 1283.62138
Summary: This paper investigates identification and estimation of a class of nonlinear panel data, single-index models. The model allows for unknown time-specific link functions, and semiparametric specification of the individual-specific effects. We develop an estimator for the parameters of interest, and propose a powerful new kernel-based modified backfitting algorithm to compute the estimator. We derive uniform rates of convergence results for the estimators of the link functions, and show the estimators of the finite-dimensional parameters are root-\(N\) consistent with a Gaussian limiting distribution. We study the small sample properties of the estimator via Monte Carlo techniques.
MSC:
| 62J02 | General nonlinear regression |
| 62G05 | Nonparametric estimation |
| 62F10 | Point estimation |
| 62F12 | Asymptotic properties of parametric estimators |
| 91B82 | Statistical methods; economic indices and measures |
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