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Computing the Distributions of Economic Models via Simulation

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  • John Stachurski
  • Vance Martin

Abstract

We study a Monte Carlo algorithm for computing marginal and stationary densities of stochastic models with the Markov property, establishing global asymptotic normality and O(n^(1/2)) convergence. Asymptotic normality is used to derive error bounds in terms of the distribution of the norm deviation. Copyright The Econometric Society 2008.

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File URL: http://hdl.handle.net/10.1111/j.0012-9682.2008.00839.x
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Bibliographic Info

Article provided by Econometric Society in its journal Econometrica.

Volume (Year): 76 (2008)
Issue (Month): 2 (03)
Pages: 443-450

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Handle: RePEc:ecm:emetrp:v:76:y:2008:i:2:p:443-450

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References

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  1. Sarno, Lucio & Valente, Giorgio, 2002. "Comparing the Accuracy of Density Forecasts from Competing Models," Computing in Economics and Finance 2002 223, Society for Computational Economics.
  2. Esteban Rossi-Hansberg & Mark L. J. Wright, 2007. "Establishment Size Dynamics in the Aggregate Economy," American Economic Review, American Economic Association, vol. 97(5), pages 1639-1666, December.
  3. A. S. Hurn & K. A. Lindsay & V. L. Martin, 2003. "On the efficacy of simulated maximum likelihood for estimating the parameters of stochastic differential Equations," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(1), pages 45-63, 01.
  4. Johnson, Paul, 2003. "A Continuous State Space Approach to “Convergence by Parts”," Vassar College Department of Economics Working Paper Series 54, Vassar College Department of Economics.
  5. Ola Elerian & Siddhartha Chib & Neil Shephard, 2000. "Likelihood inference for discretely observed non-linear diffusions," OFRC Working Papers Series 2000mf02, Oxford Financial Research Centre.
  6. Deaton, A. & Laroque, G., 1989. "On The Behavior Of Commodity Prices," Papers 145, Princeton, Woodrow Wilson School - Public and International Affairs.
  7. Nishimura, Kazuo & Rudnicki, Ryszard & Stachurski, John, 2006. "Stochastic optimal growth with nonconvexities," Journal of Mathematical Economics, Elsevier, vol. 42(1), pages 74-96, February.
  8. Hansen, Bruce E., 2005. "Exact Mean Integrated Squared Error Of Higher Order Kernel Estimators," Econometric Theory, Cambridge University Press, vol. 21(06), pages 1031-1057, December.
  9. 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.
  10. Nishimura, Kazuo & Stachurski, John, 2005. "Stability of stochastic optimal growth models: a new approach," Journal of Economic Theory, Elsevier, vol. 122(1), pages 100-118, May.
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Cited by:
  1. 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.
  2. Antunes, António & Cavalcanti, Tiago & Villamil, Anne, 2008. "Computing general equilibrium models with occupational choice and financial frictions," Journal of Mathematical Economics, Elsevier, vol. 44(7-8), pages 553-568, July.
  3. Bildirici, Melike & Ersin, Özgür, 2012. "Nonlinear volatility models in economics: smooth transition and neural network augmented GARCH, APGARCH, FIGARCH and FIAPGARCH models," MPRA Paper 40330, University Library of Munich, Germany, revised May 2012.
  4. Vance Martin & Yoshihiko Nishiyama & John Stachurski, 2011. "A Goodness Of Fit Test For Ergodic Markov Processes," KIER Working Papers 787, Kyoto University, Institute of Economic Research.
  5. R. Anton Braun & Huiyu Li & John Stachurski, 2011. "Generalized Look-Ahead Methods for Computing Stationary Densities," ANU Working Papers in Economics and Econometrics 2011-558, Australian National University, College of Business and Economics, School of Economics.

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