R.D. van Oest (Rutger)
http://repub.eur.nl/ppl/1994/
List of Publicationsenhttp://repub.eur.nl/eur_signature.png
http://repub.eur.nl/
RePub, Erasmus University RepositoryThe Davies Problem: A New Test for Random Slope in the Hierarchical Linear Model
http://repub.eur.nl/pub/78063/
Mon, 05 Jan 2015 00:00:01 GMT<div>R.D. van Oest</div><div>Ph.H.B.F. Franses</div>
__Abstract__
Crucial inference for the hierarchical linear model concerns the null hypothesis of no random slope. We argue that the usually applied statistical test suffers from the so-called Davies problem, that is, a nuisance parameter is only identified under the alternative. We propose an easy-to-implement methodology that exploits this property. We provide the relevant critical values and demonstrate through simulations that our new methodology has better power properties.Measuring changes in consumer confidence
http://repub.eur.nl/pub/13355/
Sun, 01 Jun 2008 00:00:01 GMT<div>R.D. van Oest</div><div>Ph.H.B.F. Franses</div>
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.On the econometrics of the geometric lag model
http://repub.eur.nl/pub/13350/
Tue, 01 May 2007 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.D. van Oest</div>
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.Simulation based bayesian econometric inference: principles and some recent computational advances.
http://repub.eur.nl/pub/8523/
Wed, 31 Jan 2007 00:00:01 GMT<div>L.F. Hoogerheide</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
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.Testing changes in consumer confidence indicators
http://repub.eur.nl/pub/7675/
Thu, 20 Apr 2006 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.D. van Oest</div>
The authors propose a statistical methodology to test changes in consumer confidence indicators. These indicators are surveyed monthly and each time concern
diĀ®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.Which brands gain share from which brands? Inference from store-level scanner dat
http://repub.eur.nl/pub/13799/
Thu, 01 Sep 2005 00:00:01 GMT<div>R.D. van Oest</div><div>Ph.H.B.F. Franses</div>
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.Essays on Quantitative Marketing Models and Monte Carlo Integration Methods
http://repub.eur.nl/pub/6776/
Thu, 03 Feb 2005 00:00:01 GMT<div>R.D. van Oest</div>
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.Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods
http://repub.eur.nl/pub/11191/
Wed, 01 Dec 2004 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
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.Analyzing the effects of past prices on reference price formation
http://repub.eur.nl/pub/1515/
Thu, 19 Aug 2004 00:00:01 GMT<div>R.D. van Oest</div><div>R. Paap</div>
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.On the econometrics of the Koyck model
http://repub.eur.nl/pub/1190/
Wed, 10 Mar 2004 00:00:01 GMT<div>Ph.H.B.F. Franses</div><div>R.D. van Oest</div>
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.Explaining Adaptive Radial-Based Direction Sampling
http://repub.eur.nl/pub/1045/
Thu, 07 Aug 2003 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
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.Adaptive radial-based direction sampling; Some flexible and robust Monte Carlo integration methods
http://repub.eur.nl/pub/1722/
Wed, 06 Aug 2003 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
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.Which brands gain share from which brands? Inference from store-level scanner data
http://repub.eur.nl/pub/1007/
Wed, 01 Jan 2003 00:00:01 GMT<div>R.D. van Oest</div><div>Ph.H.B.F. Franses</div>
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.A Joint Framework for Category Purchase and Consumption Behavior
http://repub.eur.nl/pub/6791/
Wed, 18 Dec 2002 00:00:01 GMT<div>R.D. van Oest</div><div>R. Paap</div><div>Ph.H.B.F. Franses</div>
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.A Dynamic Utility Maximization Model for Product Category Consumption
http://repub.eur.nl/pub/6794/
Mon, 07 Oct 2002 00:00:01 GMT<div>R.D. van Oest</div><div>Ph.H.B.F. Franses</div><div>R. Paap</div>
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.Adaptive polar sampling, a class of flexibel and robust Monte Carlo integration methods
http://repub.eur.nl/pub/555/
Tue, 17 Sep 2002 00:00:01 GMT<div>L. Bauwens</div><div>C.S. Bos</div><div>H.K. van Dijk</div><div>R.D. van Oest</div>
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.