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A Numerical Procedure to Estimate Real Business Cycle Models Using Simulated Annealing

  • Willi Semmler
  • Gang Gong
Part of the Advances in Computational Economics book series (AICE, volume 6)

Abstract

This paper presents a numerical procedure to estimate a stochastic growth model of real business cycle type where the decision rules are not analytically solvable. The statistical estimation of this type of models faces the difficulty that the relationship is not explicit between the estimated parameters and the objective function for the estimation (for example, the distance function in GMM estimation). Furthermore, multiple local optima may exist since the objective function is often nonlinear in parameters. To circumvent these problems, we introduce a global optimization algorithm, the simulated annealing, that searches the parameter space recursively for the global optimum. When this algorithm is employed, along with an appropriate approximation method of dynamic programming to numerically compute the decision rules, the estimation turns out to be efficient. The estimation of structural parameters with statistical methods appears to us a necessary step to empirically evaluate RBC models.

Keywords

Simulated Annealing Stochastic Dynamic Programming Business Cycle Theory Real Business Cycle Macroeconomic Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 1997

Authors and Affiliations

  • Willi Semmler
  • Gang Gong

There are no affiliations available

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