Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/59484
Authors: 
Corradi, Valentina
Swanson, Norman
Year of Publication: 
2011
Series/Report no.: 
Working Papers, Department of Economics, Rutgers, the State University of New Jersey 2011,12
Abstract: 
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.
Subjects: 
block bootstrap
diffusion processes
jumps
nonparametric simulated quasi maximum likelihood
parameter estimation error
recursive estimation
stochastic volatility
JEL: 
C22
C51
Document Type: 
Working Paper

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