In this paper we compare through Monte Carlo simulations the finite sample properties of estimators of the fractional differencing parameter, d. This involves frequency domain, time domain, and wavelet based approaches and we consider both parametric and semiparametric estimation methods. The...

This paper compares recently developed semiparametric estimators of Type-3 Tobit model using Monte Carlo simulations. In particular, we examine the finite sample performance of the recently proposed method by Li and Wooldridge and compare it to some alternative semiparametric estimators....

The purpose of this paper is to use the bootstrap resampling technique to calculate confidence intervals for efficiency measures and Malmquist productivity indices. The efficiency and productivity measures are obtained from non-parametric linear programming models using primal production data....

We study a very general setting, and propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general k-prospect case. We allow for the observations to be generally serially...

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...

The 2008 financial crisis has spurred investors to wonder about adding tail risk hedges to their portfolios. However, the cost of these hedges can often be a deterrent. As a consequence, many financial institutions have tried to develop cost-effective products for investors who wished to protect...

Contextual factors usually assume an important role in determining firms' productive efficiencies. Nevertheless, identifying them in a regression framework might be complicated. The problem arises from the efficiencies being correlated with each other when estimated by Data Envelopment Analysis...