http://repub.eur.nl/res/aut/1508/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl 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/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/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. http://repub.eur.nl/res/pub/2162/ 2002-04-16T00:00:00Z We examine recursive out-of-sample forecasting of monthly postwar US core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to 2 years. Correcting for the effect of explanatory variables, we still find fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s. We compare the forecasts of ARFIMAX models and ARIMAX models over the period 1984–1999. The ARIMAX(1, 1, 1) model provides the best forecasts, but its multi-step forecast intervals are too large. The multi-step forecast intervals of the ARFIMAX(0, d, 0) model prove to be more realistic. http://repub.eur.nl/res/pub/1854/ 2001-09-13T00:00:00Z Een van de onderwerpen binnen de Econometrie is het zoeken naar de structuur die schuil gaat achter macroeconomische reeksen. Dit proefschrift gaat in op twee van der gelijke reeksen, namelijk inflatie en wisselkoers reeksen. Uiteraard zijn dergelijke reeksen al vele malen geanalyseerd, met wisselend resultaat. In dit proefschrift wordt gepoogd op nieuwe wijze de data te analyseren, waarbij speciaal aandacht is voor modellen met tijdsvariÄerende parameters. Uit de gereedschapskist van de econometrie worden diverse technieken tevoorschijn gehaald die de beste (lees: meest gedetailleerde, waarheidsgetrou we) beschrijving beloven te leveren van de structuren die aan de in°atie en wisselkoers reeksen ten grondslag liggen. Hierna worden de diverse hoofdstukken, met hun conclusies, kort beschreven. http://repub.eur.nl/res/pub/6874/ 2001-02-23T00:00:00Z We examine recursive out-of-sample forecasting of monthly postwar U.S. core inflation and log price levels. We use the autoregressive fractionally integrated moving average model with explanatory variables (ARFIMAX). Our analysis suggests a significant explanatory power of leading indicators associated with macroeconomic activity and monetary conditions for forecasting horizons up to two years. Even after correcting for the effect of explanatory variables, there is conclusive evidence of both fractional integration and structural breaks in the mean and variance of inflation in the 1970s and 1980s and we incorporate these breaks in the forecasting model for the 1980s and 1990s. We compare the results of the fractionally integrated ARFIMA(0,d,0) model with those for ARIMA(1,d,1) models with fixed order of d=0 and d=1 for inflation. Comparing mean squared forecast errors, we find that the ARMA(1,1) model performs worse than the other models over our evaluation period 1984-1999. The ARIMA(1,1,1) model provides the best forecasts, but its multi-step forecast intervals are too large. http://repub.eur.nl/res/pub/6877/ 2001-02-08T00:00:00Z Internationally operating firrns naturally face the decision whether or not to hedge the currency risk implied by foreign investments. In a recent paper, Bos, Mahieu and van Dijk (2000) evaluate the returns from optimal and alternative currency hedging strategies, for a series of 7 models, using Bayesian inference and decision analysis. The models differ in the way time-varying means, variances or the unconditional error distributions are incorporated. In this extension, we compare the hedging decisions and financial returns and utilities as they result from the modelling assumptions and the attitudes towards risk. http://repub.eur.nl/res/pub/6878/ 2001-02-08T00:00:00Z We construct models which enable a decision-maker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance densities) are investigated in relation to the hedging strategies. Consequently, we can make a distinction between statistical relevance of model specifications, and the economic consequences from a risk management point of view. We compute payoffs and utilities from several alternative hedge strategies. The results indicate that modelling time varying features of exchange rate returns may lead to improved hedge behaviour within currency overlay management. http://repub.eur.nl/res/pub/1653/ 2000-11-09T00:00:00Z Internationally operating firms naturally face the decision whether or not to hedge the currency risk implied by foreign investments. In a recent paper, Bos, Mahieu and van Dijk evaluate the returns from optimal and alternative currency hedging strategies, for a series of 7 models, using Bayesian inference and decision analysis. The models differ in the way time-varying means, variances or the unconditional error distributions are incorporated. In this extension, we compare the hedging decisions and financial returns and utilities as they result from the modelling assumptions and the attitudes towards risk. http://repub.eur.nl/res/pub/1657/ 2000-08-30T00:00:00Z We construct models which enable a decision-maker to analyze the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance densities) are investigated in relation to the hedging strategies. Consequently, we can make a distinction between statistical relevance of model specifications, and the economic consequences from a risk management point of view. We compute payoffs from several alternative hedge strategies. These payoffs indicate that modelling time-varying features of exchange rate returns may lead to improved hedge behaviour within currency overlay management. http://repub.eur.nl/res/pub/11335/ 2000-01-01T00:00:00Z We construct models which enable a decision maker to analyse the implications of typical time series patterns of daily exchange rates for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy-tailed disturbance densities) are investigated in relation to the hedging strategies. Consequently, we can make a distinction between statistical relevance of model specifications and the economic consequences from a risk management point of view. We compute payoffs and utilities from several alternative hedge strategies. The results indicate that modelling time-varying features of exchange rate returns may lead to improved hedge behaviour within currency overlay management. http://repub.eur.nl/res/pub/13508/ 1999-11-11T00:00:00Z A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears. http://repub.eur.nl/res/pub/7712/ 1999-10-21T00:00:00Z Adaptive Polar Sampling (APS) is proposed as a Markov chain Monte Carlo method for Bayesian analysis of models with ill-behaved posterior distributions. In order to sample efficiently from such a distribution, a location-scale transformation and a transformation to polar coordinates are used. After the transformation to polar coordinates, a Metropolis-Hastings algorithm is applied to sample directions and, conditionally on these, distances are generated by inverting the CDF. A sequential procedure is applied to update the location and scale. Tested on a set of canonical models that feature near non-identifiability, strong correlation, and bimodality, APS compares favourably with the standard Metropolis-Hastings sampler in terms of parsimony and robustness. APS is applied within a Bayesian analysis of a GARCH-mixture model which is used for the evaluation of the Value-at-Risk of the return of the Dow Jones stock index. http://repub.eur.nl/res/pub/1605/ 1999-10-13T00:00:00Z Exchange rates typically exhibit time-varying patterns in both means and variances. The histograms of such series indicate heavy tails. In this paper we construct models which enable a decision-maker to analyze the implications of such time series patterns for currency risk management. Our approach is Bayesian where extensive use is made of Markov chain Monte Carlo methods. The effects of several model characteristics (unit roots, GARCH, stochastic volatility, heavy tailed disturbance densities) are investigated in relation to the hedging decision strategies. Consequently, we can make a distinction between statistical relevance of model specifications, and the economic consequences from a risk management point of view. The empirical results suggest that econometric modelling of heavy tails and time-varying means and variances pays off compared to a efficient markets model. The different ways to measure persistence and changing volatilities appear to strongly influence the hedging decision the investor faces. http://repub.eur.nl/res/pub/1550/ 1998-07-02T00:00:00Z Adaptive Polar Sampling is proposed as an algorithm where random drawings are directly generated from the target function (posterior) in all-but-one directions of the parameter space. The method is based on the mixed integration technique of Van Dijk, Kloek & Boender (1985) but extends this one by replacing the one-dimensional quadrature step by Monte Carlo simulation from this one-dimensional distribution function. The method is particularly suited for the analysis of ill-behaved surfaces. An illustrative example shows the feasibility of the algorithm. http://repub.eur.nl/res/pub/1556/ 1998-07-02T00:00:00Z A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears. http://repub.eur.nl/res/pub/7759/ 1998-02-27T00:00:00Z A key application of long memory time series models concerns inflation. Long memory implies that shocks have a long-lasting effect. It may however be that empirical evidence for long memory is caused by neglecting one or more level shifts. Since such level shifts are not unlikely for inflation, where the shifts may be caused by sudden oil price shocks, we examine whether evidence for long memory (indicated by the relevance of an ARFIMA model) in G7 inflation rates is spurious or exaggerated. Our main findings are that apparent long memory is quite resistant to level shifts, although for a few inflation rates we find that evidence for long memory disappears.