Computing the Distributions of Economic Models Via Simulation
AbstractThis paper studies the convergence properties of a Monte Carlo algorithm for computing distributions of state variables when the underlying model is a Markov chain with absolutely continuous transition probabilities. We show that the L1 error of the estimator always converges to zero with probability one. In addition, rates of convergence are established for L1 and integral mean squared errors. The algorithm is shown to have many applications in economics.
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Bibliographic InfoPaper provided by The University of Melbourne in its series Department of Economics - Working Papers Series with number 949.
Length: 25 pages
Date of creation: 2005
Date of revision:
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Other versions of this item:
- 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 & University of Melbourne, 2006. "Computing the Distributions of Economic Models via Simulation," Computing in Economics and Finance 2006 185, Society for Computational Economics.
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-03-18 (All new papers)
- NEP-CMP-2006-03-18 (Computational Economics)
- NEP-ECM-2006-03-18 (Econometrics)
- NEP-ICT-2006-03-18 (Information & Communication Technologies)
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