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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- Brock, William A. & Mirman, Leonard J., 1972. "Optimal economic growth and uncertainty: The discounted case," Journal of Economic Theory, Elsevier, vol. 4(3), pages 479-513, June.
- S. Rao Aiyagari, 1993.
"Uninsured idiosyncratic risk and aggregate saving,"
502, Federal Reserve Bank of Minneapolis.
- 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 & Vance Martin, 2008. "Computing the Distributions of Economic Models via Simulation," Econometrica, Econometric Society, vol. 76(2), pages 443-450, 03.
- John Stachurski, 2006. "Computing the Distributions of Economic Models Via Simulation," KIER Working Papers 615, Kyoto University, Institute of Economic Research.
- John Stachurski, 2005. "Computing the Distributions of Economic Models Via Simulation," Department of Economics - Working Papers Series 949, The University of Melbourne.
- Chib, Siddhartha & Nardari, Federico & Shephard, Neil, 2002. "Markov chain Monte Carlo methods for stochastic volatility models," Journal of Econometrics, Elsevier, vol. 108(2), pages 281-316, June.
- Lars Peter Hansen & Thomas J. Sargent, 2007. "Introduction to Robustness," Introductory Chapters, in: Robustness Princeton University Press.
- Danielsson, Jon, 1994. "Stochastic volatility in asset prices estimation with simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 375-400.
- Durham, Garland B & Gallant, A Ronald, 2002. "Numerical Techniques for Maximum Likelihood Estimation of Continuous-Time Diffusion Processes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 297-316, July.
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