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Computing the Distributions of Economic Models Via Simulation

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  • John Stachurski

    ()
    (Department of Economics, University of Melbourne)

Abstract

This paper studies a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov process. It is shown that the L1 error of the estimator always converges to zero with probability one, and often at a parametric rate. A related technique for computing stationary distributions is also investigated.

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File URL: http://www.kier.kyoto-u.ac.jp/DP/DP615.pdf
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Bibliographic Info

Paper provided by Kyoto University, Institute of Economic Research in its series KIER Working Papers with number 615.

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Length: 34pages
Date of creation: Apr 2006
Date of revision:
Handle: RePEc:kyo:wpaper:615

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Keywords: Distributions; Markov processes; simulation.;

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References

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  1. Esteban Rossi-Hansberg & Mark L. J. Wright, 2007. "Establishment Size Dynamics in the Aggregate Economy," American Economic Review, American Economic Association, American Economic Association, vol. 97(5), pages 1639-1666, December.
  2. Deaton, A. & Laroque, G., 1989. "On The Behavior Of Commodity Prices," Papers, Princeton, Woodrow Wilson School - Public and International Affairs 145, Princeton, Woodrow Wilson School - Public and International Affairs.
  3. Neil Shephard & Ola Elerian & Siddhartha Chib, 1998. "Likelihood inference for discretely observed non-linear diffusions," Economics Series Working Papers 1998-W10, University of Oxford, Department of Economics.
  4. 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.
  5. Johnson, Paul A., 2005. "A continuous state space approach to "Convergence by Parts"," Economics Letters, Elsevier, Elsevier, vol. 86(3), pages 317-321, March.
  6. Brock, William A. & Mirman, Leonard J., 1972. "Optimal economic growth and uncertainty: The discounted case," Journal of Economic Theory, Elsevier, Elsevier, vol. 4(3), pages 479-513, June.
  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. Nishimura, Kazuo & Stachurski, John, 2005. "Stability of stochastic optimal growth models: a new approach," Journal of Economic Theory, Elsevier, Elsevier, vol. 122(1), pages 100-118, May.
  9. Sarno, Lucio & Valente, Giorgio, 2002. "Comparing the Accuracy of Density Forecasts from Competing Models," Computing in Economics and Finance 2002, Society for Computational Economics 223, Society for Computational Economics.
  10. Hansen, Bruce E., 2005. "Exact Mean Integrated Squared Error Of Higher Order Kernel Estimators," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 21(06), pages 1031-1057, December.
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Cited by:
  1. 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.
  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. Richard Anton Braun & Huiyu Li & John Stachurski, 2009. "Computing Densities: A Conditional Monte Carlo Estimator," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo CIRJE-F-678, CIRJE, Faculty of Economics, University of Tokyo.
  4. R. Anton Braun & Huiyu Li & John Stachurski, 2011. "Generalized Look-Ahead Methods for Computing Stationary Densities," ANU Working Papers in Economics and Econometrics, Australian National University, College of Business and Economics, School of Economics 2011-558, Australian National University, College of Business and Economics, School of Economics.
  5. Vance Martin & Yoshihiko Nishiyama & John Stachurski, 2011. "A Goodness Of Fit Test For Ergodic Markov Processes," KIER Working Papers, Kyoto University, Institute of Economic Research 787, Kyoto University, Institute of Economic Research.

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