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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-181.
Length: 25 pages
Date of creation: Oct 2009
Date of revision:
Other versions of this item:
- Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities: A Conditional Monte Carlo Estimator," CIRJE F-Series CIRJE-F-678, CIRJE, Faculty of Economics, University of Tokyo.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
- Lars Peter Hansen & Thomas J. Sargent, 2007. "Introduction to Robustness," Introductory Chapters, 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.
- Aiyagari, S Rao, 1994.
"Uninsured Idiosyncratic Risk and Aggregate Saving,"
The Quarterly Journal of Economics,
MIT Press, vol. 109(3), pages 659-84, August.
- 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.
- John Stachurski & Vance Martin, 2008.
"Computing the Distributions of Economic Models via Simulation,"
Econometric Society, vol. 76(2), pages 443-450, 03.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ().
If references are entirely missing, you can add them using this form.