We analyse the demand for food in the USA and the Netherlands in the period 1929-88, using a differential consumer demand system, the CBS model, and we compare our results with those of Tobin. For the USA we find an income elasticity of 0-75, both in budget-survey data and in time-series data,...

In this paper we examine a model of cointegration where long-run parameters are subject to switching between several different cointegrating regimes. These shifts are allowed to be governed by the outcome of an unobserved Markov chain with unknown transition probabilities. We illustrate this...

This paper develops a semi-parametric estimation method for hurdle (two-part) count regression models. The approach in each stage is based on Laguerre series expansion for the unknown density of the unobserved heterogeneity. The semi-parametric hurdle model nests Poisson and negative binomial...

Using sequential trend break and panel data models, we investigate the unit root hypothesis for the inflation rates of thirteen OECD countries. With individual country tests, we find evidence of stationarity in only four of the thirteen countries. The results are more striking with the panel...

The purpose of this paper is to estimate the patent equation, an empirical counterpart to the 'knowledge-production function'. Innovation output is measured through the number of European patent applications and the input by research capital, in a panel of French manufacturing firms. Estimating...

This paper explains the organization of our experiment in applied econometrics, where participating teams had one year (1 July 1995 to 1 August 1996) to answer specific economic questions with a specified data set, but using their own methodology and economic insights. In the end, eight teams...

Cross-validation is a method used to estimate the expected prediction error of a model. Such estimates may be of interest in themselves, but their use for model selection is more common. Unfortunately, cross-validation is viewed as being computationally expensive in many situations. In this...