http://repub.eur.nl/res/aut/719/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/8822/ 2007-02-22T00:00:00Z Governance in de Nederlandse gezondheidszorg is ingewikkeld. Zorgaanbieders moeten werken in de combinatie van private, publieke en professionals governance. Onderzocht is hoe die combinatie in elkaar zit en hoe governance in de zorg functioneert. Governance bestaat doorgaans uit spelregels en omgangsvormen voor bestuur, toezicht en verantwoording. Deze spelregels en omgangsvormen moeten ervoor zorgen dat organisaties of personen de belangen, die aan hen zijn toevertrouwd, zo goed mogelijk behartigen. Iedere sector in de maatschappij heeft tegenwoordig met governance te maken. Ook de gezondheidszorg. In Nederland wordt zorg en behandeling geleverd door private organisaties en personen. Organisaties zoals ziekenhuizen, verpleeghuizen en thuiszorgorganisaties. Personen zoals huisartsen, medisch specialisten en fysiotherapeuten. Zorg wordt verleend op basis van private contracten tussen zorgaanbieder, zorgvrager en zorgverzekeraar. Zorgaanbieders hebben hun eigen private belangen, zoals de continuïteit van de eigen organisatie. Maar ze moeten ook bijdragen aan de publieke belangen die de overheid voor de gezondheidszorg wil behartigen. En ze dienen rekening te houden met de professionele opvattingen over zorgverlening en de belangen van de zorgprofessionals. Private, publieke en professionele belangen vragen ieder hun eigen governance. Er worden immers verschillende doelen gesteld. Er heersen verschillende morele opvattingen en omgangsvormen, in deze studie aangeduid als moraliteiten. Er gelden verschillende governance principes en er worden verschillende instrumenten gebruikt. Moraliteiten, doelen, principes en instrumenten van private, publieke en professionals governance zijn onderling zo verschillend, dat gesproken kan worden over verschillende governance werelden. Zorgaanbieders moeten functioneren in de combinatie van die drie governance werelden. Ze zijn genoodzaakt steeds afwegingen te maken tussen – en binnen – private, publieke en professionele belangen. Ze hebben te voldoen aan de eisen van de drie governance werelden. Zowel de doelen als de inrichting van de governance kunnen onderling strijdig zijn. In deze studie is onderzocht hoe de combinatie van de private, publieke en professionals governance wereld functioneert voor Nederlandse zorgaanbieders. http://repub.eur.nl/res/pub/1589/ 1999-04-26T00:00:00Z Asset liability management (ALM) is an important and challenging problem for institutional investors and financial intermediaries. The requirement to fulfill its liablilities constrains the institutional investor in its asset allocation possiblilites. We formulate an ALM model for pension funds as a multistage stochastic programming model. Relevant variables such as future inflation rates, stock retruns, and bond yields are unknown. This is incorporated in the ALM model by means of an event tree, which represents the expected development of the economic variables as well as the corresponding uncertainty. The event tree is constructed by sampling from a time series model for the variables, and is therefore subject to sampling uncertainty. Moreover, for the event tree to be realistic, it is required not to exhibit arbitrage opportunies. In ths paper we examine the effect of sampling uncertainty and the structure of the event tree on the optimal policies. Furthermore, we consider the effect of random sampling and the tree structure on the probability of arbitragefree trees. We also compare the optimal solutions to the ALM problem for trees with an without arbitrage. For these purposes, we consider data from a Dutch pension fund. http://repub.eur.nl/res/pub/7799/ 1997-05-08T00:00:00Z In this paper, we make use of state space models to investigate the presence of stochastic trends in economic time series. A model is specified where such a trend can enter either in the autoregressive representation or in a separate state equation. Tests based on the former are analogous to Dickey-Fuller tests of unit roots, while the latter are analogous to KPSS tests of trend-stationarity. We use Bayesian methods to survey the properties of the likelihood function in such models and to calculate posterior odds ratios comparing models with and without stochastic trends. In addition, we extend these ideas to the problem of testing for integration at seasonal frequencies and show how techniques can be used to carry out Bayesian variants of HEGY test or the Canova-Hansen test. http://repub.eur.nl/res/pub/7822/ 1997-01-23T00:00:00Z Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters. Priors exploiting this feature result in regular posteriors, while priors which neglect it result in posteriori favor of nonidentified parameter values. By considering the implicit AR representation of an ARMA model a prior with the desired proporties is obtained. The implicit AR representation also allows to construct easily implemented algorithms to analyse ARMA parameters. As a byproduct, posteriors odds ratios can be computed to compare (nonnested) parsimonious ARMA models. The procedures are applied to two datasets, the (extended) Nelson-Plosser data and monthly observations of US 3-month and 10 year interest rates. For approximately 50% of the series in these two datasets an ARMA model is favored above an AR model. http://repub.eur.nl/res/pub/13251/ 1997-01-01T00:00:00Z In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using several simulated and empirical data. http://repub.eur.nl/res/pub/2102/ 1997-01-01T00:00:00Z Examples of descriptive models for changing seasonal patterns in economic time series are autoregressive models with seasonal unit roots or with deterministic seasonal mean shifts. In this paper we show through a forecasting comparison for three macroeconomic time series (for which tests indicate the presence of seasonal unit roots) that allowing for possible seasonal mean shifts can improve forecast performance. Next, by means of simulation we demonstrate the impact of imposing an incorrect model on forecasting. We find that an inappropriate decision can deteriorate forecasting performance dramatically in both directions, and hence we recommend the practitioner to take account of seasonal mean shifts when testing for seasonal unit roots. http://repub.eur.nl/res/pub/11310/ 1995-01-01T00:00:00Z This paper has two themes. First, we classify some effects which outliers in the data have on unit root inference. We show that, both in a classical and a Bayesian framework, the presence of additive outliers moves ‘standard’ inference towards stationarity. Second, we base inference on an independent Student-t instead of a Gaussian likelihood. This yields results that are less sensitive to the presence of outliers. Application to several time series with outliers reveals a negative correlation between the unit root and degrees of freedom parameter of the Student-t distribution. Therefore, imposing normality may incorrectly provide evidence against the unit root. http://repub.eur.nl/res/pub/1354/ 1995-01-01T00:00:00Z In this paper we propose a Bayesian analysis of seasonal unit roots in quarterly observed time series. Seasonal unit root processes are useful to describe economic series with changing seasonal fluctuations. A natural alternative model for similar purposes contains deterministic seasonal mean shifts instead of seasonal stochastic trends. This leads to analysing seasonal unit roots in the presence of mean shifts using Bayesian techniques. Our method is illustrated using several simulated and empirical data. http://repub.eur.nl/res/pub/1363/ 1995-01-01T00:00:00Z Parameters in AutoRegressive Moving Average (ARMA) models are locally nonidentified, due to the problem of root cancellation. Parameters can be constructed which represent this identification problem. We argue that ARMA parameters should be analyzed conditional on these identifying parameters. Priors exploiting this feature result in regular posteriors, while priors which neglect it result in posteriori favor of nonidentified parameter values. By considering the implicit AR representation of an ARMA model a prior with the desired proporties is obtained. The implicit AR representation also allows to construct easily implemented algorithms to analyze ARMA parameters. As a byproduct, posteriors odds ratios can be computed to compare (nonnested) parsimonious ARMA models. The procedures are applied to two datasets, the (extended) Nelson-Plosser data and monthly observations of US 3-month and 10 year interest rates. For approximately 50% of the series in these two datasets an ARMA model is favored above an AR model.