Estimating a density function over a bounded domain can be very complicated and resulting in an unsatisfactory or unrealistic density estimate. In many cases a one-to-one transformation can be applied to the considered data set, but there are also situations where such a unique transformation...

In this paper two techniques, long memory and panel data models, are combined in order to increase the power of unit root tests. The power is shown to be always better against fractional alternatives and usually against autoregressive alternatives. The test is then used to reanalyze data sets...

A bank that lends money to a household faces two types of risk. Most commonly mentioned is the risk of a default. Hardly ever referred to is the risk of an early redemption of the loan - leading to dormancy. We model consumer loans' transition from an active to a dormant state and estimate a...

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....

This paper shows that the bootstrap algorithm for average technical efficiency by Atkinson and Wilson (1995) should be applied with great care for the Data Envelopment Analysis (DEA) estimator if the production frontier is stochastic. A stochastic frontier implies that the DEA estimator is...

This paper presents and applies different approaches to estimate returns to scale in multiple-input muliple-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...

This paper presents a Monte Carlo simulation study of the bootstrap algorithm proposed by Löthgren and Tambour (1997) for calculation of bootstrap confidence intervals for the firm-specific Data Envelopment Analysis (DEA) Malmquist productivity index. The simulation results indicate that the...

This paper evaluates the performance of three bootstrap algorithms for the data envelopment analysis (DEA) estimator using a Monte Carlo simulation study. The Löthgren and Tambour (1997) (LT) algorithm; the Simar and Wilson (1997b) (SW) algorithm; and a combination of the LT and SW algorithms...

The double bootstrap provides a useful tool for bootstrapping approximately pivotal quantities by using an "inner" bootstrap loop to estimate the variance. When the estimators are computationally intensive, the double bootstrap may become infeasible. We propose the use of a new variance...

In this paper we propose a general method for testing the Granger noncausality hypothesis in stationary nonlinear models of unknown functional form. These tests are based on a Taylor expansion of the nonlinear model around a given point in a sample space. We study the performance of our tests by...