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Predictive density construction and accuracy testing with multiple possibly misspecified diffusion models

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  • Corradi, Valentina
  • Swanson, Norman R.

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. 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 Salanié (2004). In an empirical illustration, the predictive densities from several models of the one-month federal funds rates are compared.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Econometrics.

Volume (Year): 161 (2011)
Issue (Month): 2 (April)
Pages: 304-324

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Handle: RePEc:eee:econom:v:161:y:2011:i:2:p:304-324

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Web page: http://www.elsevier.com/locate/jeconom

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Keywords: Block bootstrap Diffusion processes Jumps Nonparametric simulated quasi maximum likelihood Parameter estimation error Recursive estimation Stochastic volatility;

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References

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Cited by:
  1. Norman R. Swanson & Lili Cai, 2011. "In- and Out-of-Sample Specification Analysis of Spot Rate Models: Further Evidence for the Period 1982-2008," Departmental Working Papers, Rutgers University, Department of Economics 201102, Rutgers University, Department of Economics.
  2. Norman Swanson & Richard Urbach, 2013. "Prediction and Simulation Using Simple Models Characterized by Nonstationarity and Seasonality," Departmental Working Papers, Rutgers University, Department of Economics 201323, Rutgers University, Department of Economics.

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