How to Maximize the Likelihood Function for a DSGE Model
CREATES Research Paper 2008-32
32 Pages Posted: 27 Feb 2008
Date Written: June 19, 2008
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%.
Keywords: Simulated Annealing, Resampling, CMA-ES, CMA-ES optimization routine,Likelihood function, Multimodel objective function, Non-convex search space, Resampling, The Nelder-Mead simplex routine
JEL Classification: C61, C88, E30
Suggested Citation: Suggested Citation