No abstract

No abstract

No abstract

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 result in substantially better inferences when applied to f-ratios rather than to standard errors.

No abstract

No abstract

No abstract

This paper develops a new semiparametric approach for the estimation of hazard functions in the presence of unobserved heterogeneity+ The hazard function is specified parametrically, whereas the distribution of the unobserved heterogeneity is indirectly estimated using the method of kernels+ The semiparametric efficiency bounds are derived+ The estimator obtains these bounds in large samples+ The authors thank