Neyman and Scott (1948) define the incidental parameter problem. In panel data with T observations per individual and unobservable individual-specific effects, the inconsistency of the maximum likelihood estimator of the common parameters is in general of the order 1/T. This paper considers the...

Neyman and Scott (1948) define the incidental parameter problem. In panel data with T observations per individual and unobservable individual-specific effects, the maximum likelihood estimator of the common parameters is in general inconsistent. This paper develops the integrated moment...

We reconsider the efficiency bound for the semi-parametric Mixed Proportional Hazard (MPH) model with parametric baseline hazard and regression function. This bound was first derived by Hahn (1994). One of his results is that if the baseline hazard is Weibull, the efficiency bound is singular,...

This paper develops the minimal asymptotic distributions for estimators of panel data models with incidental parameters.

This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and time-varying regressors. In particular, this paper allows for discrete measurement of the durations as happens often in practice. The integrated baseline hazard and all...

This paper considers dynamic time series binary choice models. It proves near epoch dependence and strong mixing for the dynamic binary choice model with correlated errors. Using this result, it shows in a time series setting the validity of the dynamic probit likelihood procedure when lags of...

Applied researchers often need to estimate confidence intervals for functions of parameters, such as the effects of counterfactual policy changes. If the function is continuously differentiable and has non-zero and bounded derivatives, then they can use the delta method. However, if the function...

This paper proposes an estimator for the endogenous switching regression models with fixed effects. The estimator allows for endogenous selection and for conditional heteroscedasticity in the outcome equation. Applying the estimator to a dataset on the productivity in agriculture substantially...

This paper presents a new estimator for the mixed proportional hazard model that allows for a nonparametric baseline hazard and time-varying regressors. In particular, this paper allows for discrete measurement of the durations as happens often in practice. The integrated baseline hazard and all...

This paper gives a test of overidentifying restrictions that is robust to many instruments and heteroskedasticity. It is based on a jackknife version of the overidentifying test statistic. Correct asymptotic critical values are derived for this statistic when the number of instruments grows...