Computing Densities: A Conditional Monte Carlo Estimator

R. Braun, Huiyu Li and John Stachurski ()

No CARF-F-181, CARF F-Series from Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo

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.

Pages: 25 pages
Date: 2009-10
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https://www.carf.e.u-tokyo.ac.jp/old/pdf/workingpaper/fseries/187.pdf (application/pdf)

Related works:
Working Paper: Computing Densities: A Conditional Monte Carlo Estimator (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:cfi:fseres:cf181

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