Computing Densities: A Conditional Monte Carlo Estimator
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.
|Date of creation:||Oct 2009|
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- John Stachurski & Vance Martin, 2008.
"Computing the Distributions of Economic Models via Simulation,"
Econometric Society, vol. 76(2), pages 443-450, 03.
- John Stachurski, 2005. "Computing the Distributions of Economic Models Via Simulation," Department of Economics - Working Papers Series 949, The University of Melbourne.
- 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.
- John Stachurski, 2006. "Computing the Distributions of Economic Models Via Simulation," KIER Working Papers 615, Kyoto University, Institute of Economic Research.
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