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Computing Densities: A Conditional Monte Carlo Estimator

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  • Richard Anton Braun

    (Faculty of Economics, University of Tokyo)

  • Huiyu Li

    (Graduate School of Economics, University of Tokyo)

  • John Stachurski

    (Institute of Economic Research, Kyoto University)

Abstract

We propose a generalized conditional Monte Carlo technique for computing densities in economic models. Global consistency and functional asymptotic normality are established under ergodicity assumptions on the simulated process. The asymptotic normality result allows us to characterize the asymptotic distribution of the error in density space, and implies faster convergence than nonparametric kernel density estimators. We show that our results nest several other well-known density estimators, and illustrate potential applications.

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File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2009/2009cf678.pdf
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Bibliographic Info

Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-678.

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Length: 25pages
Date of creation: Oct 2009
Date of revision:
Handle: RePEc:tky:fseres:2009cf678

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  1. John Stachurski & University of Melbourne, 2006. "Computing the Distributions of Economic Models via Simulation," Computing in Economics and Finance 2006 185, Society for Computational Economics.
  2. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
  3. Danielsson, Jon, 1994. "Stochastic volatility in asset prices estimation with simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 375-400.
  4. Nishimura, Kazuo & Stachurski, John, 2005. "Stability of stochastic optimal growth models: a new approach," Journal of Economic Theory, Elsevier, vol. 122(1), pages 100-118, May.
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