Department of Economics and Business Economics

How to Maximize the Likelihood Function for a DSGE Model

Research output: Working paperResearch

  • Martin Møller Andreasen, Denmark
  • School of Economics and Management
This paper extends two optimization routines to deal with objective functions for DSGE models.
The optimization routines are i) a version of Simulated Annealing developed by Corana, Marchesi
& Ridella (1987), and ii) the evolutionary algorithm CMA-ES developed by Hansen, Müller &
Koumoutsakos (2003). Following these extensions, we examine the ability of the two routines to
maximize the likelihood function for a sequence of test economies. Our results show that the CMA-
ES routine clearly outperforms Simulated Annealing in its ability to find the global optimum and in
efficiency. With 10 unknown structural parameters in the likelihood function, the CMA-ES routine
finds the global optimum in 95% of our test economies compared to 89% for Simulated Annealing.
When the number of unknown structural parameters in the likelihood function increases to 20 and
35, then the CMA-ES routine finds the global optimum in 85% and 71% of our test economies,
respectively. The corresponding numbers for Simulated Annealing are 70% and 0%.
Original languageEnglish
Place of publicationAarhus
PublisherInstitut for Økonomi, Aarhus Universitet
Number of pages30
Publication statusPublished - 2008

    Research areas

  • CMA-ES optimization routine, Multimodel objective function, Nelder-Mead simplex routine, Non-convex search space, Resampling, Simulated Annealing

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ID: 11667973