One of the most common practical problems in statistics and econometrics is the estimation of linear regression models with heteroscedastic errors. This article reports the results of a Monte Carlo comparison of various parametric and semiparametric estimated generalized least squares (EGLS)...

This paper considers nonparametric kernel estimation of models with generated regressors and derives the asymptotic distribution of the resulting estimators. It is also shown how generated regressors may be used to reduce the dimensionality of certain nonparametric models. A labor supply...

A simple saddlepoint (SP) approximation for the distribution of generalized empirical likelihood (GEL) estimators is derived. Simulations compare the performance of the SP and other methods such as the Edgeworth and the bootstrap for special cases of GEL: continuous updating, empirical...

Incorrect specification of the hazard rate in duration analysis can produce inconsistent estimators of the parameters of the model. We propose a new estimator for discrete duration models in which the hazard rate is comprised of an inner index function of the covariates and time variable and an...

The usual standard errors for the regression coefficients in a seemingly unrelated regression model have a substantial downward bias. Bootstrapping the standard errors does not seem to improve inferences. In this paper, Monte Carlo evidence is reported which indicates that bootstrapping can...

It is well known that additional moment conditions can lead to more efficient instrumental variables estimates of an econometric model. This paper shows how the auxiliary regressions implied by the derivatives of a nonlinear model can generate additional moment conditions and that these can be...