Rien Wagenvoort

Infrastructure finance in Europe: Composition, evolution and crisis impact.

This article is the first attempt to compile comprehensive data on infrastructure finance in Europe. We decompose infrastructure finance by institutional sector (i.e. public versus private) into its main components, which consist of traditional public procurement, project finance and finance by the corporate sector, and analyse how the roles of the public and private sectors in financing infrastructure have evolved over time, especially during the recent economic and financial crisis. In contrast with government finance that is slightly up, private finance, in particular project finance through Publi-Private Partnerships, has fallen substantially during the recent crisis, reversing, at least temporarily, the longer-term trend of more private and less public financing of infrastructure.

On B-robust instrumental variable estimation of the linear model with panel data

The aim of this paper is to demonstrate how to acquire robust consistent estimates of the linear model when the fundamental orthogonality condition is not fulfilled. With this end in view, we develop two estimation procedures: Two stage generalized M (2SGM) and robust generalized method of moments (RGMM). Both estimators are B-robust, i.e. their associated influence function is bounded, consistent and asymptotic normally distributed. Our simulation results indicate that the relatively efficient RGMM estimator (in regressions with heteroskedastic and/or autocorrelated errors) provides accurate parameter estimates of a panel data model with all variables subject to measurement errors, even if a substantial portion of the data is contaminated with aberrant observations. The traditional estimation techniques such as 2SLS and GMM break down when outliers corrupt the data.

A Recursive Thick Frontier Approach to Estimating Production Efficiency

We introduce a new panel data estimation technique for cost and production functions: the Recursive Thick Frontier Approach (RTFA). RTFA has two advantages over existing thick frontier methods. First, technical inefficiency is allowed to be dependent on the explanatory variables of the frontier model. Secondly, no distributional assumptions are imposed on the inefficiency component of the error term. We show by means of simulation experiments that RTFA can outperform the popular stochastic frontier approach (SFA) and the “within” OLS estimator for realistic parameterisations of the productivity model.

On the computation and efficiency of a HBP-GM estimator: some simulation results

Abstract We propose and test a specific correction factor which improves both the resampling and projection algorithm for approximating the minimum volume ellipsoid (MVE) estimator. Simulations show that a high-breakdown-point GM estimator, based among other things on these improved MVE-estimates of location and scatter (i) is little less efficient than OLS if data are free from outlying observations, but in most cases is much more efficient if outliers corrupt the data, (ii) is always more efficient than Rousseeuws least median of squares (LMS) estimator, and (iii) is always superior to both LMS and OLS if both precision and efficiency are considered.

Risk Preference and Indirect Utility in Portfolio-Choice Problems

We consider a portfolio-choice problem with one risky and one safe asset, where the utility function exhibits decreasing absolute risk aversion (DARA). We show that the indirect utility function of the portfolio-choice problem need not exhibit DARA. However, if the (optimal) marginal propensity to invest is positive for both assets, which is true when the utility function exhibits nondecreasing relative risk aversion, then the DARA property is carried over from the direct to the indirect utility function.

Identifying all distinct sample p-p plots, with an application to the exact finite sample distribution of the L1-FCvM test statistic

P-p plots contain all the information that is needed for scale-invariant comparisons. Indeed, Empirical Distribution Function (EDF) tests translate sample p-p plots into a single number. In this paper we characterize the set of all distinct p-p plots for two balanced sample of size n absent ties. Distributions of EDF test statistics are embedded in this set. It is thus used to derive the exact finite sample distribution of the L1-version of the Fisz-Cramer-von Mises test. Comparing this distribution with the (known) limiting distribution shows that the latter can always be used for hypothesis testing: although for finite samples the critical percentiles of the limiting distribution differ from the exact values, this will not lead to differences in the rejection of the underlying hypothesis.

On the exact finite sample distribution of the L1-FCvM test statistic

We derive the exact finite sample distribution of the L1 -version of the Fisz–Cramér–von Mises test statistic (FCvM 1). We first characterize the set of all distinct sample p-p plots for two balanced samples of size n absent ties. Next, we order this set according to the corresponding value of FCvM 1. Finally, we link these values to the probabilities that the underlying p-p plots emerge. Comparing the finite sample distribution with the (known) limiting distribution shows that the latter can always be used for hypothesis testing: although for finite samples the critical percentiles of the limiting distribution differ from the exact values, this will not lead to differences in the rejection of the underlying hypothesis.

A K-Sample Homogeneity Test Based on the Quantification of the p-p Plot: The Harmonic Weighted Mass Index

We propose a quantification of the p-p plot that assigns equal weight to all distances between the respective distributions: the surface between the p-p plot and the diagonal. This surface is labelled the Harmonic Weighted Mass (HWM) index. We introduce the diagonal-deviation (d-d) plot that allows the index to be computed exactly under all circumstances. For two balanced samples absent ties the finite sample distribution of the HWM index is derived. Simulations show that in most cases unbalanced samples and ties have little effect on this distribution. The d-d plot allows for a straightforward extension to the K-sample HWM index. As we have not been able to derive the distribution of the index for K>2, we simulate significance tables for K=3,...,15. An example involving economic growth rates of the G7 countries illustrates that the HWM test can have better power than alternative Empirical Distribution Function tests.