http://repub.eur.nl/res/aut/6112/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/37301/ 2012-09-25T00:00:00Z We study risk perception and actual decision-making by the corporate elite, where we consider CEOs, CFOs and non-executives. We collect data for many members of the elite for Netherlands-based companies using the vignettes method. We find that CEOs are more risk tolerant but do not act accordingly by demanding higher returns. CFOs and non-executives are found to be more risk-averse; but, interestingly, only the non-executives demand higher returns more than CEOs do. Differences in demanded returns across CEOs and CFOs are found to be negligible. When decision makers mature and get more experienced, they tend to ask higher returns on investment. For all members of the corporate elite it holds that overconfidence is consistently related to higher risk tolerance, whereas those degrees of overconfidence are similar. http://repub.eur.nl/res/pub/23954/ 2010-04-01T00:00:00Z We analyze the revision history of quarterly and monthly (seasonally unadjusted) macroeconomic variables for the Netherlands, Ireland, Luxemburg and the United States, where we focus on the degree of deterministic seasonality in these revisions. We document that the data show most deterministic seasonality in the final revision. The first-release data and the in-between revisions show a variety of seasonal patterns. The consequences of these findings for the interpretation and modeling of macroeconomic data are discussed. http://repub.eur.nl/res/pub/14584/ 2009-01-29T00:00:00Z Monitoring involves the collection, analysis and evaluation of information over time. For many professionals, monitoring is a central aspect of their work. For example, policy- makers closely watch the e®ects of their current policies to set the right course for reform. Likewise, physicians monitor the well-being of their patients to adjust their treatments when necessary. In business, ¯nancial investors monitor stock prices and interest rates to optimally time their investments, while marketing managers watch their customers' needs and wants to frame their marketing e®orts. The above examples illustrate that monitoring is crucial in many disciplines to make the right decisions at the right moment. For this reason, there has always been a need for improved monitoring methods. With the advent of increasingly powerful computers and advanced analytical techniques, monitoring systems can nowadays process large amounts of information and have become fully automated where desired. A large body of moni- toring methods originate from academics. Especially during the past four decades, many insights from various ¯elds such as economics, statistics, psychometrics and econometrics found their way into everyday monitoring practice. With the overwhelming availability of information in some cases, but also the intrinsic lack of information in other cases, the area is continuously faced with new and highly relevant research challenges. The aim of this thesis is to contribute to the development of new monitoring methods by o®ering potential solutions to some of these challenges. The challenges studied in this thesis arise from all three aspects of monitoring, that is from the collection, the analysis as well as from the evaluation of information. http://repub.eur.nl/res/pub/13055/ 2008-08-25T00:00:00Z Several lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented. http://repub.eur.nl/res/pub/17279/ 2008-08-01T00:00:00Z http://repub.eur.nl/res/pub/12211/ 2008-04-14T00:00:00Z We analyze five vintages of eighteen quarterly macroeconomic variables for the Netherlands and we focus on the degree of deterministic seasonality in these series. We document that the data show most such deterministic seasonality for their first release vintage and for the last available vintage. In between vintages show a variety of seasonal patterns. We show that seasonal patterns in later vintages can hardly be predicted by those in earlier vintages. The consequences of these findings for the interpretation and modeling of macroeconomic data are discussed. http://repub.eur.nl/res/pub/11892/ 2008-03-31T00:00:00Z This paper puts forward a data collection method to measure weekly consumer confidence at the individual level. The data thus obtained allow to statistically analyze the dynamic correlation of such a consumer confidence indicator and to draw inference on transition rates, which is not possible for currently available monthly data collected by statistical agencies on the basis of repeated cross-sections. An application of the method to various waves of data for the Netherlands shows its merits. Upon temporal aggregation we also show the resemblance of our data with those collected by Statistics Netherlands. http://repub.eur.nl/res/pub/16385/ 2008-01-01T00:00:00Z Several lessons learnt from a Bayesian analysis of basic macroeconomic time series models are presented for the situation where some model parameters have substantial posterior probability near the boundary of the parameter region. This feature refers to near-instability within dynamic models, to forecasting with near-random walk models and to clustering of several economic series in a small number of groups within a data panel. Two canonical models are used: a linear regression model with autocorrelation and a simple variance components model. Several well-known time series models like unit root and error correction models and further state space and panel data models are shown to be simple generalizations of these two canonical models for the purpose of posterior inference. A Bayesian model averaging procedure is presented in order to deal with models with substantial probability both near and at the boundary of the parameter region. Analytical, graphical and empirical results using U.S. macroeconomic data, in particular on GDP growth, are presented. http://repub.eur.nl/res/pub/9230/ 2007-03-20T00:00:00Z We develop a formal statistical approach to investigate the possibility that leading indicator variables have different lead times at business cycle peaks and troughs. For this purpose, we propose a novel Markov switching vector autoregressive model, where economic growth and leading indicators share a common Markov process determining the state, but such that their cycles are non-synchronous with the non-synchronicity varying across the different regimes. An empirical application to monthly US industrial production (IP) and The Conference Board's Composite Index of Leading Indicators (CLI) for the period 1959-2004 shows that on average the CLI leads IP by more than seven months at peaks, but only by three and a half months at troughs. In terms of timeliness, the CLI is therefore most useful for signalling oncoming recessions. Furthermore, we find that allowing for asymmetric lead times leads to improved real-time dating of business cycle peaks and troughs and more accurate forecasts of turning points and IP growth. http://repub.eur.nl/res/pub/7945/ 2006-08-28T00:00:00Z Several lessons learned from a Bayesian analysis of basic economic time series models by means of the Gibbs sampling algorithm are presented. Models include the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model, the Instrumental Variables model and as Hierarchical Linear Mixed Models, the State-Space model and the Panel Data model. We discuss issues involved when drawing Bayesian inference on regression parameters and variance components, in particular when some parameter have substantial posterior probability near the boundary of the parameter region, and show that one should carefully scan the shape of the posterior density function. Analytical, graphical and empirical results are used along the way. http://repub.eur.nl/res/pub/7743/ 2006-03-21T00:00:00Z We present a road map for effective application of Bayesian analysis of a class of well-known dynamic econometric models by means of the Gibbs sampling algorithm. Members belonging to this class are the Cochrane-Orcutt model for serial correlation, the Koyck distributed lag model, the Unit Root model and as Hierarchical Linear Mixed Models, the State-Space model and the Panel Data model. We discuss issues involved when drawing Bayesian inference on equation parameters and variance components and show that one should carefully scan the shape of the criterion function for irregularities before applying the Gibbs sampler. Analytical, graphical and empirical results are used along the way.