Abstract
A semiparametric fixed effects model is introduced to describe the nonlinear trending phenomenon in panel data analysis and it allows for the cross-sectional dependence in both the regressors and the residuals. A pooled semiparametric profile likelihood dummy variable approach based on the first-stage local linear fitting is developed to estimate both the parameter vector and the nonlinear time trend function. As both the time series length T and the cross-sectional size N tend to infinity, the resulting estimator of the parameter vector is asymptotically normal with a root-(NT) convergence rate. Meanwhile, the asymptotic distribution for the nonparametric estimator of the trend function is also established with a root-(NTh) convergence rate. Two simulated examples are provided to illustrate the finite sample performance of the proposed method. In addition, the proposed model and estimation method are applied to a CPI data set as well as an input-output data set. (C) 2012 Elsevier B.V. All rights reserved.
| Original language | English |
|---|---|
| Pages (from-to) | 71-85 |
| Number of pages | 15 |
| Journal | Journal of Econometrics |
| Volume | 171 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Nov 2012 |
Keywords
- Nonlinear time trend
- Local linear fitting
- COEFFICIENT
- Profile likelihood
- INFERENCE
- TIME-SERIES
- Panel data
- Cross-sectional dependence
- Semiparametric regression