This paper presents and applies different approaches to estimate returns to scale in multiple-input multiple-output technologies. Scale efficiency gives quantitative information of scale characteristics. A primal based approach to estimate the scale elasticity is proposed as an alternative to...

This paper considers the problem of choosing the number of bootstrap repetitions B for bootstrap standard errors, confidence intervals, and tests. For each of these problems, the paper provides a threestep method for choosing B to achieve a desired level of accuracy. Accuracy is measured by the...

In recent years, several new parametric and nonparametric bootstrap methods have been proposed for time series data. Which of these methods should applied researchers use? We provide evidence that for many applications in time series econometrics parametric methods are more accurate, and we...

In the quarterly Danish sample based wage statistics firm within a given industry are randomly selected with probabilities proportional to size. The hourly mean wage per employee is registered within each selected firm. Then the population mean per employee is estimated by the pps-estimator,...

We perform an analysis of tail index estimation through Monte-Carlo simulations of synthetic data, in order to evaluate several tail estimators proposed in the literature. We derive and discuss the error of the Hill estimator under a general tail expansion of the distribution function. The...

The bootstrap, which provides powerful approximations for many classes of statistics, is studied for simple linear rank statistics employing bounded and smooth score functions. To verify consistency we view a rank statistic as a statistic induced by a statistical functional (formula) which is...

The least-absolute-deviations (LAD) estimator for a median- regression model does not satisfy the standard conditions for obtaining asymptotic refinements through use of the bootstrap because the LAD objective function is not smooth. This paper overcomes this problem by smoothing the objective...

The optimal minimum distance (OMD) estimator for models of covariance structures is asymptotically efficient but has much worse finite-sample properties than does the equally-weighted minimum distance (EWMD) estimator. This paper shows how the bootstrap can be used to improve the finite-sample...