Semiparametric identification in panel data discrete response models

Binary Response Models for Panel Data: Identification and Information

Semiparametric Trending Panel Data Models with Cross-Sectional Dependence

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 nonparametric ...

Semiparametric Efficient Estimation of AR(1) Panel Data Models∗

This study focuses on the semiparametric efficient estimation of random effect panel models containing AR(1) disturbances. We also consider such estimators when the effects and regressors are correlated (Hausman and Taylor, 1981). We introduce two semiparametric efficient estimators that make minimal assumptions on the distribution of the random errors, effects, and the regressors and that prov...

Semiparametric Estimation of Regression Models for Panel Data

Linear models with error components are widely used to analyze panel data. Some applications of these models require knowledge of the probability densities of the error components. Existing methods handle this requirement by assuming that the densities belong to known parametric families of distributions (typically the normal distribution). This paper shows how to carry out nonparametric estima...

Semiparametric EÆcient Estimation of Dynamic Panel Data Models

This paper extends the semiparametric eÆcient treatment of panel data models pursued by Park and Simar (1994) and Park, Sickles, and Simar (1998, 2003) to a dynamic panel setting. We develop a semiparametric eÆcient estimator under minimal assumptions when the panel model contains a lagged dependent variable. We apply this new estimator to analyze the structure of demand between city pairs for ...