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Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models
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DOI：10.1016/j.jeconom.2010.12.009.png)
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Abstract
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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. We then construct 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 Salanie (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared. (C) 2011 Elsevier B.V. All rights reserved.
Keywords:
Block bootstrap
Diffusion processes
Jumps
Nonparametric simulated quasi maximum likelihood
Parameter estimation error
Recursive estimation
Stochastic volatility
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