Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/59484 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
Working Paper No. 2011-12
Verlag: 
Rutgers University, Department of Economics, New Brunswick, NJ
Zusammenfassung: 
This paper develops tests for comparing the accuracy of predictive densities derived from (possibly misspecified) diffusion models. In particular, we first outline a simple simulation-based framework for constructing predictive densities for one-factor and stochastic volatility models. Then, we construct accuracy assessment tests that are in the spirit of Diebold and Mariano (1995) and White (2000). In order to establish the asymptotic properties of our tests, we also develop a recursive variant of the nonparametric simulated maximum likelihood estimator of Fermanian and Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.
Schlagwörter: 
block bootstrap
diffusion processes
jumps
nonparametric simulated quasi maximum likelihood
parameter estimation error
recursive estimation
stochastic volatility
JEL: 
C22
C51
Dokumentart: 
Working Paper

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