Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood estimation. An alternative approach, pseudolikelihood estimation, decouples estimation of the error component structure and the production frontier, has been...

Monte Carlo (MC) simulations are one of the dominant approaches to compare statistical methods. To date, there is no standard procedure for MC simulations. Although internally valid, they exhibit a certain degree of arbitrariness through the various choices that researchers make. In this paper,...

Based on the seminal paper of Farrell (1957), researchers have developed several methods for measuring effi ciency. Nowadays, the most prominent representatives are nonparametric data envelopment analysis (DEA) and parametric stochastic frontier analysis (SFA), both introduced in the late 1970s....

In this paper we study the finite sample behavior of the Hill estimator under α-stable distributions. Using large Monte Carlo simulations we show that the Hill estimator overestimates the true tail exponent and can hardly be used on samples with small length. Utilizing our results, we introduce...

In certain circumstances, both researchers and policy makers are faced with the challenge of determining individual efficiency scores for each decision making unit (DMU) under consideration. In this study, we use a Monte Carlo experimentation to analyze the optimal approach to determining...

Though theoretically appealing and very popular amongst labour economists, the interpretation of the unexplained part of the Oaxaca (1973) decomposition as discrimination rather than an omitted variable problem in cross-section data has often been criticised. In this note it is shown that this...

In this paper we propose a simulation algorithm for the Schöbel-Zhu (1999) model and its extension to include stochastic interest rates, the Schöbel-Zhu-Hull-White model as considered in Van Haastrecht et al. (2009). Both schemes are derived by analyzing the lessons learned from the Andersen...

Monte Carlo methods are simulation algorithms to estimate a numerical quantity in a statistical model of a real system. These algorithms are executed by computer programs. Variance reduction techniques (VRT) are needed, even though computer speed has been increasing dramatically, ever since the...