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Computing the Distributions of Economic Models via Simulation

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Author Info
John Stachurski
University of Melbourne

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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 $L_1$ 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 investigate

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Publisher Info
Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 185.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:185

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Related research
Keywords: Distributions; Markov processes; simulation;

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Find related papers by JEL classification:
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions
C63 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming - - - Computational Techniques

References listed on IDEAS
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.:

  1. Angus Deaton & Guy Laroque, 1990. "On The Behavior of Commodity Prices," NBER Working Papers 3439, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. 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, Blackwell Publishing, vol. 24(1), pages 45-63, 01. [Downloadable!] (restricted)
  3. 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. [Downloadable!]
  4. Johnson, Paul A., 2005. "A continuous state space approach to "Convergence by Parts"," Economics Letters, Elsevier, vol. 86(3), pages 317-321, March. [Downloadable!] (restricted)
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  5. 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. [Downloadable!] (restricted)
  6. Giorgio Valente & Lucio Sarno, 2004. "Comparing the accuracy of density forecasts from competing models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(8), pages 541-557. [Downloadable!]
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  7. 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. [Downloadable!] (restricted)
  8. Elerain, Ola & Chib, Siddhartha & Shephard, Neil, 2001. "Likelihood Inference for Discretely Observed Nonlinear Diffusions," Econometrica, Econometric Society, vol. 69(4), pages 959-93, July.
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  9. Esteban Rossi-Hansberg & Mark L. J. Wright, 2006. "Establishment size dynamics in the aggregate economy," Staff Report 382, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  10. Nishimura, Kazuo & Rudnicki, Ryszard & Stachurski, John, 2006. "Stochastic optimal growth with nonconvexities," Journal of Mathematical Economics, Elsevier, vol. 42(1), pages 74-96, February. [Downloadable!] (restricted)
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Cited by:
(explanations, 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.)

  1. António Antunes & Tiago Cavalcanti & Anne Villamil, 2006. "Computing General Equilibrium Models with Occupational Choice and Financial Frictions," SCAPE Policy Research Working Paper Series 0611, National University of Singapore, Department of Economics, SCAPE. [Downloadable!]
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