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dc.contributor.authorShen, Xiangjinen_US
dc.contributor.authorTsurumi, Hirokien_US
dc.description.abstractWe compare Bayesian and sample theory model specification criteria. For the Bayesian criteria we use the deviance information criterion and the cumulative density of the mean squared errors of forecast. For the sample theory criterion we use the conditional Kolmogorov test. We use Markov chain Monte Carlo methods to obtain the Bayesian criteria and bootstrap sampling to obtain the conditional Kolmogorov test. Two non-nested models we consider are the CIR and Vasicek models for spot asset prices. Monte Carlo experiments show that the DIC performs better than the cumulative density of the mean squared errors of forecast and the CKT. According to the DIC and the mean squared errors of forecast, the CIR model explains the daily data on uncollateralized Japanese call rate from January 1 1990 to April 18 1996; but according to the CKT, neither the CIR nor Vasicek models explains the daily data.en_US
dc.publisherDep. of Economics, Rutgers, the State Univ. of New Jersey New Brunswick, NJen_US
dc.relation.ispartofseriesWorking Papers, Department of Economics, Rutgers, the State University of New Jersey 2011,26en_US
dc.subject.keyworddeviance information criterionen_US
dc.subject.keywordcumulative density of the mean squared errors of forecasten_US
dc.subject.keywordMarkov chain Monte Carlo algorithmsen_US
dc.subject.keywordblock bootstrapen_US
dc.subject.keywordgeneralized methods of momentsen_US
dc.subject.keywordconditional Kolmogorov testen_US
dc.subject.keywordCIR and Vasicek modelsen_US
dc.subject.stwCapital Asset Pricing Modelen_US
dc.subject.stwStochastischer Prozessen_US
dc.titleComparison of Bayesian model selection criteria and conditional Kolmogorov test as applied to spot asset pricing modelsen_US
dc.typeWorking Paperen_US
Appears in Collections:Working Papers, Department of Economics, Rutgers University

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