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SIMANN: A Global Optimization Algorithm using Simulated Annealing

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  • Goffe William L.

    ()
    (Department of Economics and International Business University of Southern Mississippi)

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    Abstract

    This paper describes SIMANN, a Fortran and GAUSS implementation of the simulated annealing algorithm. The Fortran code was used in "Global Optimization of Statistical Functions with Simulated Annealing" (Goffe, Ferrier, and Rogers 1994). In that paper, simulated annealing was found to be competitive, if not superior, to multiple restarts of conventional optimization routines for difficult optimization problems. This paper compares SIMANN to the DFP algorithm on another optimization problem, namely, the maximum likelihood estimation of a rational expectations model, which was previously studied in the literature. SIMANN again performs quite well, and shows several advantages over DFP. This paper also describes simulated annealing, and gives explicit directions and an example for using the included GAUSS and Fortran code.

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    File URL: http://www.degruyter.com/view/j/snde.1996.1.3/snde.1996.1.3.1020/snde.1996.1.3.1020.xml?format=INT
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    Bibliographic Info

    Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

    Volume (Year): 1 (1996)
    Issue (Month): 3 (October)
    Pages: 1-9

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    Handle: RePEc:bpj:sndecm:v:1:y:1996:i:3:n:al1

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    Web page: http://www.degruyter.com

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    Cited by:
    1. Franke, Reiner & Jang, Tae-Seok & Sacht, Stephen, 2012. "Moment matching versus Bayesian estimation: Backward-looking behaviour in a New-Keynesian baseline model," Economics Working Papers 2012-08, Christian-Albrechts-University of Kiel, Department of Economics.
    2. K. Chua & S. Ong, 2013. "Test of misspecification with application to negative binomial distribution," Computational Statistics, Springer, vol. 28(3), pages 993-1009, June.
    3. William L. Goffe, . "A Toolkit for Optimizing Functions in Economics," Computing in Economics and Finance 1997 65, Society for Computational Economics.
    4. Coenen, Günter & Lombardo, Giovanni & Smets, Frank & Straub, Roland, 2008. "International transmission and monetary policy cooperation," Working Paper Series 0858, European Central Bank.
    5. André Kurmann, 2004. "Maximum Likelihood Estimation of Dynamic Stochastic Theories with an Application to New Keynesian Pricing," Macroeconomics 0409028, EconWPA.
    6. Kurmann, Andre, 2007. "VAR-based estimation of Euler equations with an application to New Keynesian pricing," Journal of Economic Dynamics and Control, Elsevier, vol. 31(3), pages 767-796, March.
    7. Ueda, Atsuko, 2008. "Dynamic model of childbearing and labor force participation of married women: Empirical evidence from Korea and Japan," Journal of Asian Economics, Elsevier, vol. 19(2), pages 170-180, April.
    8. Franke, Reiner, 2013. "Competitive Moment Matching of a New-Keynesian and an Old-Keynesian Model," Annual Conference 2013 (Duesseldorf): Competition Policy and Regulation in a Global Economic Order 79988, Verein für Socialpolitik / German Economic Association.
    9. Lombardo, Giovanni & McAdam, Peter, 2012. "Financial market frictions in a model of the euro area," Working Paper Series 1423, European Central Bank.
    10. Nakatsuma, Teruo, 2000. "Bayesian analysis of ARMA-GARCH models: A Markov chain sampling approach," Journal of Econometrics, Elsevier, vol. 95(1), pages 57-69, March.
    11. Roger A. McCain, 2000. "Road Rage: Imitative Learning Of Self-Destructive Behavior In An Agent-Based Simulation," Computing in Economics and Finance 2000 270, Society for Computational Economics.
    12. Schündeln, Matthias, 2005. "Modeling Firm Dynamics to Identify the Cost of Financing Constraints in Ghanaian Manufacturing," Proceedings of the German Development Economics Conference, Kiel 2005 29, Verein für Socialpolitik, Research Committee Development Economics.

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