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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 & Vance Martin, 2008. "Computing the Distributions of Economic Models via Simulation," Econometrica, Econometric Society, vol. 76(2), pages 443-450, 03.
  2. Aiyagari, S Rao, 1994. "Uninsured Idiosyncratic Risk and Aggregate Saving," The Quarterly Journal of Economics, MIT Press, vol. 109(3), pages 659-84, August.
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