Lam and Schoeni (1993) consider an equation where earnings are explained by schooling and ability. They assume that ability data are lacking and that schooling is measured with error. The estimate obtained by regressing earnings on schooling thus contains omitted variable bias (OVB), which is...

The jackknife is a resampling method that uses subsets of the original database by leaving out one observation at a time from the sample. The paper outlines a procedure to obtain jackknife estimates for several inequality indices with only a few passes through the data. The number of passes is...

This article examines basic features of ranked Conjoint-data, analyzes the adequacy of evaluation methods and proposes improvements for better utilizing the information provided by ranked data. It is shown that commonly used goodness-of-fit measures provide inadequate proxy measures for...

CJ-Analysen werden in der Marktforschung eingesetzt, um metrische Nutzenfunktionen zu ermitteln. Sofern die Eingabedaten in Form einer Rangordnung vorliegen, ist bei der Auswertung eine Skalentransformation vorzunehmen. Am Beispiel einer Conjoint-Anwendung wird aufgezeigt, daß hieraus deutliche...

This paper argues that forecast estimators should minimise the loss function in a statistical, rather than deterministic, way. We introduce two new elements into the classical econometric analysis: a subjective guess on the variable to be forecasted and a probability reflecting the confidence...

Testing and estimating the rank of a matrix of estimated parameters is key in a large variety of econometric modelling scenarios. This paper describes general methods to test for and estimate the rank of a matrix, and provides details on a variety of modelling scenarios in the econometrics...

Under inflation targeting inflation exhibits negative serial correlation in the United Kingdom, and little or no persistence in Canada, Sweden and New Zealand, and estimates of the indexation parameter in hybrid New Keynesian Phillips curves are either equal to zero, or very low, in all...