http://repub.eur.nl/res/aut/1994/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/13355/ 2008-06-01T00:00:00Z Consumer confidence indicators are surveyed monthly and each month concern different individuals. This complicates a straightforward interpretation of shifts in confidence. First, it is not clear how many respondents switch from and to negative, neutral and positive opinions in consecutive months. Second, reported net changes in confidence may be largely driven by the different respondent samples used over time. The proposed methodology addresses both issues. It involves estimating unobserved switching between negative, neutral and positive opinions for what can be thought of as being the same set of individuals. Next, a new change-in-confidence measure is developed from these switching proportions and the associated confidence bounds are computed for testing purposes. Applications to US and Dutch confidence data show that US respondents tend to switch attitudes more often than their Dutch counterparts do. Furthermore, the illustrations show that monthly changes in consumer confidence are not often significantly different from zero. Hence, claims about increased or decreased confidence should be made with care. http://repub.eur.nl/res/pub/13350/ 2007-05-01T00:00:00Z In this letter we focus on the econometrics of the geometric distributed lag model, after application of the so-called Koyck transformation. The Koyck transformation entails a parameter restriction, which should not be overlooked for reasons of estimation efficiency. Furthermore, the t statistic for the parameter for direct effects has a non-standard distribution. We provide solutions to these two issues. http://repub.eur.nl/res/pub/8523/ 2007-01-31T00:00:00Z In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the Metropolis-Hastings algorithm and Gibbs sampling (being the most popular Markov chain Monte Carlo methods) and importance sampling. After that, we discuss two recently developed sampling methods: adaptive radial based direction sampling [ARDS], which makes use of a transformation to radial coordinates, and neural network sampling, which makes use of a neural network approximation to the posterior distribution of interest. Both methods are especially useful in cases where the posterior distribution is not well-behaved, in the sense of having highly non-elliptical shapes. The simulation techniques are illustrated in several example models, such as a model for the real US GNP and models for binary data of a US recession indicator. http://repub.eur.nl/res/pub/7675/ 2006-04-20T00:00:00Z The authors propose a statistical methodology to test changes in consumer confidence indicators. These indicators are surveyed monthly and each time concern di®erent individuals. This complicates a straightforward interpretation of changes in the values of the index. The proposed methodology involves estimating the transition matrix which connects the fractions of positive, neutral and negative opinions. The elements of this matrix can be estimated and confidence bounds can be computed. A by-product of the method is a simple tool to correct for seasonality. An illustration to about two decades of Dutch data shows that monthly changes in consumer confidence are not often significantly different from zero. http://repub.eur.nl/res/pub/13799/ 2005-09-01T00:00:00Z Market share models for weekly store-level data are useful to understand competitive structures by delivering own and cross price elasticities. These models can however not be used to examine which brands lose share to which brands during a specific period of time. It is for this purpose that we propose a new model, which does allow for such an examination. We illustrate the model for two product categories in two markets, and we provide share-switching estimates. We also demonstrate how our model can be used to decompose own and cross price elasticities. http://repub.eur.nl/res/pub/6776/ 2005-02-03T00:00:00Z The last few decades have led to an enormous increase in the availability of large detailed data sets and in the computing power needed to analyze such data. Furthermore, new models and new computing techniques have been developed to exploit both sources. All of this has allowed for addressing research questions via analyses which were infeasible to carry out previously. This thesis builds on both the modeling and the computing developments. The first part contains three quantitative marketing models. These models can be applied to scanner data to get a better understanding of purchase behavior of households and to infer the effectiveness of promotions on brand performance. The second part of the thesis provides an overview of several Monte Carlo techniques which can be used in Bayesian analyses to get insight into the posterior density of model parameters. Additionally, it describes a new methodology which extends current methods. http://repub.eur.nl/res/pub/11191/ 2004-12-01T00:00:00Z Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with non-elliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformation a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. The ARDS algorithms are illustrated on a regression model with scale contamination and a mixture model for economic growth of the USA. http://repub.eur.nl/res/pub/1515/ 2004-08-19T00:00:00Z We propose a new reference price framework for brand choice. In this framework, we employ a Markov-switching process with an absorbing state to model unobserved price recall of households. Reference prices result from the prices households are able to remember. Our model can be used to learn how many prices observed in the past are used for reference price formation. Furthermore, we learn to what extent households have sufficient price knowledge to form an internal reference price. For A.C. Nielsen scanner panel data on catsup purchases, we find that the prices observed at the previous purchase occasion have an average recall probability of about 20%. Furthermore, the average probability that a household has sufficient price knowledge to form a reference price is estimated at about 30%. Even though price recall is very limited the impact of reference price formation on brand choice is substantial, and it is stronger than two popular alternative models in the literature suggest. Moreover, contrary to the two alternative models, our model does not suggest asymmetry between price gains and losses. http://repub.eur.nl/res/pub/1190/ 2004-03-10T00:00:00Z The geometric distributed lag model, after application of the so-called Koyck transformation, is often used to establish the dynamic link between sales and advertising. This year, the Koyck model celebrates its 50th anniversary.In this paper we focus on the econometrics of this popular model,and we show that this seemingly simple model is a little more complicated than we always tend to think. First, the Koyck transformation entails a parameter restriction, which should not be overlooked for efficiency reasons. Second, the t-statistic for the parameter for direct advertising effects has a non-standard distribution. We provide solutions to these two issues. For the monthly Lydia Pinkham data, it is shown that various practical decisions lead to very different conclusions. http://repub.eur.nl/res/pub/1045/ 2003-08-07T00:00:00Z In this short paper we summarize the computational steps of Adaptive Radial-Based Direction Sampling (ARDS), which can be used for Bayesian analysis of ill behaved target densities. We consider one simulation experiment in order to illustrate the good performance of ARDS relative to the independence chain MH algorithm and importance sampling. http://repub.eur.nl/res/pub/1722/ 2003-08-06T00:00:00Z Adaptive radial-based direction sampling (ARDS) algorithms are specified for Bayesian analysis of models with nonelliptical, possibly, multimodal target distributions. A key step is a radial-based transformation to directions and distances. After the transformations a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to evaluate generated directions. Next, distances are generated from the exact target distribution by means of the numerical inverse transformation method. An adaptive procedure is applied to update the initial location and covariance matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the ARDS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. The empirical examples include a regression model with scale contamination and a mixture model for economic growth of the USA. http://repub.eur.nl/res/pub/1007/ 2003-01-01T00:00:00Z Market share models for weekly store-level data are useful to understand competitive structures by delivering own and cross price elasticities. These models can however not be used to examine which brands lose share to which brands during a specific period of time. It is for this purpose that we propose a new model, which does allow for such an examination. We illustrate the model for two product categories in two markets, and we show that our model has validity in terms of both in-sample fit and out-of-sample forecasting. We also demonstrate how our model can be used to decompose own and cross price elasticities to get additional insights into the competitive structure. http://repub.eur.nl/res/pub/6791/ 2002-12-18T00:00:00Z We propose a consistent utility-based framework to jointly explain a household's decisions on purchase incidence, brand choice and purchase quantity. The approach differs from other approaches, currently available in the literature, as it is able to take into account consumption dynamics. In the model, households derive utility from consumption, and they relate their purchase behavior to consumption planning. We illustrate our model for yogurt purchases, and show that our model yields important additional insights. One such insight is that the reservation price of households is not fixed, but depends on the available inventory stock. Furthermore, we find that promotional activities increase sales through more purchases in the product category and brand switching, but the effect through larger purchase quantities is limited. http://repub.eur.nl/res/pub/6794/ 2002-10-07T00:00:00Z It is conceivable that the "whether to buy" and "how much to buy" decisions in the purchasing process of households are influenced by the inventory process. In this paper we therefore put forward a model for consumption, where we rely on established economic theory. We incorporate this model in a model for purchase behavior. Our consumption specification, which is derived from utility maximization principles, is more flexible than an ad hoc approach, which has recently been proposed in the literature. We illustrate our model for yogurt purchases, and show that our model yields important additional and useful insights. One such insight is that promotion anticipation behavior turns out not only to occur in the purchasing process, but also in the consumption process. http://repub.eur.nl/res/pub/555/ 2002-09-17T00:00:00Z Adaptive Polar Sampling (APS) algorithms are proposed for Bayesian analysis of models with nonelliptical, possibly, multimodal posterior distributions. A location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings method or, alternatively, an importance sampling method is applied to sample directions and, conditionally on these, distances are generated by inverting the cumulative distribution function. A sequential procedure is applied to update the initial location and scaling matrix in order to sample directions in an efficient way. Tested on a set of canonical mixture models that feature multimodality, strong correlation, and skewness, the APS algorithms compare favourably with the standard Metropolis-Hastings and importance samplers in terms of flexibility and robustness. APS is applied to several econometric and statistical examples. The empirical results for a regression model with scale contamination, an ARMA-GARCH-Student t model with near cancellation of roots and heavy tails, a mixture model for economic growth, and a nonlinear threshold model for industrial production growth confirm the practical flexibility and robustness of APS.