http://repub.eur.nl/res/aut/5919/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/13406/ 2006-01-01T00:00:00Z Een halve eeuw geleden werd in Rotterdam het Econometrisch Instituut opgericht. Naar aanleiding van dit jubileum kijken we naar hoe het gebruik van modellen in de econometrie is geevolueerd. http://repub.eur.nl/res/pub/6876/ 2001-02-12T00:00:00Z Using US data from June 1984 to July 1999, we show that the impact of firm-specific characteristics like size and book-to-price on future excess stock returns varies considerably over time. The impact can be either positive or negative at different times. This time variation is partially predictable. We investigate whether the partial predictability signals security mispricing or risk compensation by formulating alternative modeling strategies. The strategies are compared empirically, In particular, we allow for a state-dependent choice of investment styles rather than a once-and-for-all choice for a particular style, for example based on high book-to-price ratios or small market cap values. Using alternative ways to correct for risk, we find significant and robust excess returns to style rotating investment strategies. Business cycle oriented approaches exhibit the best overall performance. Purely statistical models for style rotation or fixed investment styles reveal less robust behavior. http://repub.eur.nl/res/pub/13824/ 1998-11-01T00:00:00Z We consider econometric modeling of weekly observed scanning data on a fast moving consumer good (FMCG), with a specific focus on the relationship between market share, distribution, advertising, price, and promotion. Such data can show non-stationary characteristics. Therefore, we use cointegration techniques to quantify the long-run effects of marketing efforts. Since weekly scanning data can contain aberrant observations due to, e.g., out-of-stock situations or measurement errors, we favor an outlier robust cointegration method, which we outline in detail. In our illustrative FMCG example, we find different results across robust and non-robust methods for the long-run marketing effects. http://repub.eur.nl/res/pub/1390/ 1996-01-01T00:00:00Z In this paper we consider empirical econometric models for nine brands of fast-moving nondurable consumer product using weekly observed scanning data on market share and distribution conditional on advertising, price, and promotion activities. Since the data show nonstationary characteristics, we rely on cointegration techniques to estimate long-run and short-run parameters. Additionally, as there are many outlying observations in our weekly scanning data, we apply robust cointegration methods. We find different results across robust and non-robust methods for the long-run relations between market share and distribution and for the short-run response to disequilibrium situations. http://repub.eur.nl/res/pub/2089/ 1995-01-01T00:00:00Z A periodic cointegration model is proposed to describe quarterly observed consumption. This model allows the cointegrating vectors and the adjustment parameters to vary with the seasons. Its links are discussed with an often considered standard economic theoretical model for macroeconomic variables like consumption. A simple empirical model specification strategy is given and applied to Austrian consumption and income data. http://repub.eur.nl/res/pub/11232/ 1985-01-01T00:00:00Z A flexible numerical integration method is proposed for the computation of moments of a multivariate posterior density with different tail properties in different directions. The method (called mixed integration) amounts to a combination of classical numerical integration and Monte Carlo integration. Mixed integration is parsimonious in the sense that is makes use of the same parameters as the more restrictive multivariate normal importance function. The method is applied in order to compute the posterior scores of three candidates for a professorship in operations research, taking into account four different decision criteria. http://repub.eur.nl/res/pub/11229/ 1983-01-01T00:00:00Z The posterior distribution of a small-scale illustrative econometric model is used to compare symmetric simple importance sampling with asymmetric simple importance sampling. The numerical results include posterior first and second order moments, numerical error estimates of the first order moments, posterior modes, univariate marginal posterior densities and bivariate marginal posterior densities plotted in three-dimensional figures. http://repub.eur.nl/res/pub/11227/ 1980-01-01T00:00:00Z An earlier paper [Kloek and Van Dijk (1978)] is extended in three ways. First, Monte Carlo integration is performed in a nine-dimensional parameter space of Klein's model I [Klein (1950)]. Second, Monte Carlo is used as a tool for the elicitation of a uniform prior on a finite region by making use of several types of prior information. Third, special attention is given to procedures for the construction of importance functions which make use of nonlinear optimization methods. *1 This paper started as a revision of Van Dijk and Kloek (1978). In the course of the work our ideas developed to such an extent that the final result is an almost completely new paper. We are indebted to a referee for a number of very useful suggestions. We also wish to thank A.S. Louter and G. den Broeder of the Econometric Institute for their help in preparing the necessary computer programs. http://repub.eur.nl/res/pub/11228/ 1980-01-01T00:00:00Z This paper discusses inferential procedures for the family of stable distributions, when the data are tabulated in the form of interval frequencies. The estimation criteria used are minimum chi-square and multinomial maximum likelihood. In evaluating the theoretical probabilities corresponding to the intervals, use is made of the inversion theorem for characteristic functions. Chi-square tail probabilities for independent samples are pooled by means of theKolmogorov statistic. As an illustration, the methods are applied to Dutch and Australian income data. http://repub.eur.nl/res/pub/11224/ 1978-01-01T00:00:00Z Monte Carlo (MC) is used to draw parameter values from a distribution defined on the structural parameter space of an equation system. Making use of the prior density, the likelihood, and Bayes' Theorem it is possible to estimate posterior moments of both structural and reduced form parameters. The MC method allows a rather liberal choice of prior distributions. The number of elementary operations to be preformed need not be an explosive function of the number of parameters involved. The method overcomes some existing difficulties of applying Bayesian methods to medium size models. The method is applied to a small scale macro model. The prior information used stems from considerations regarding short and long run behavior of the model and form extraneous observations on empirical long term ratios of economic variables. Likelihood contours for several parameter combinations are plotted, and some marginal posterior densities are assessed by MC. http://repub.eur.nl/res/pub/11225/ 1978-01-01T00:00:00Z The parameters of several families of distributions are estimated by means of minimum χ2; use is made of random samples taken from Dutch income-earning groups in 1973. The numerical search routine used, is the Complex method due to Box. The χ2 function is evaluated by standard numerical integration procedures. The lognormal and the Gamma families are rejected because of a poor fit. The log t and the log Pearson IV families are introduced. This results in a considerable improvement of χ2 critical levels. The generalized Gamma and the Champernowne function describe the income distribution reasonably well in some cases. http://repub.eur.nl/res/pub/15312/ 1975-01-01T00:00:00Z An extension or modification of the output of least-squares computer subroutines is proposed in order to enable the researcher, who is confronted with an unreasonable parameter estimate, to investigate the effect of changing this value on the other parameter estimates and the estimated residual variance by simple hand computation.