Computing Densities: A Conditional Monte Carlo Estimator
AbstractWe 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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Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-678.
Date of creation: Oct 2009
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Other versions of this item:
- Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities: A Conditional Monte Carlo Estimator," CARF F-Series CARF-F-181, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- NEP-ALL-2009-11-27 (All new papers)
- NEP-ECM-2009-11-27 (Econometrics)
- NEP-ETS-2009-11-27 (Econometric Time Series)
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- John Stachurski & University of Melbourne, 2006.
"Computing the Distributions of Economic Models via Simulation,"
Computing in Economics and Finance 2006
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