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A COMPARISON OF NUMERICAL METHODS FOR THE SOLUTION OF CONTINUOUS-TIME DSGE MODELS

Published online by Cambridge University Press:  23 May 2017

Juan Carlos Parra-Alvarez
Juan Carlos Parra-Alvarez*
Affiliation:
Aarhus University and CREATES
*
Address correspondence to: Juan Carlos Parra-Alvarez, Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark; e-mail: [email protected].
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Abstract

This study evaluates the accuracy of a set of techniques that approximate the solution of continuous-time Dynamic Stochastic General Equilibrium models. Using the neoclassical growth model, I compare linear-quadratic, perturbation, and projection methods. All techniques are applied to the Hamilton–Jacobi–Bellman equation and the optimality conditions that define the general equilibrium of the economy. Two cases are studied depending on whether a closed-form solution is available. I also analyze how different degrees of non-linearities affect the approximated solution. The results encourage the use of perturbations for reasonable values of the structural parameters of the model and suggest the use of projection methods when a high degree of accuracy is required.

Keywords

Continuous-Time DSGE ModelsLinear-Quadratic ApproximationPerturbation MethodProjection Method
Type
Articles
Information
Macroeconomic Dynamics , Volume 22 , Issue 6 , September 2018 , pp. 1555 - 1583
Copyright
Copyright © Cambridge University Press 2017 

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Footnotes

I would like to thank the editor and two anonymous referees for extremely useful comments and suggestions, as well as the participants of the 18th Annual Conference on Computing in Economics and Finance in Prague, the 6th CSDA International Conference on Computational and Financial Econometrics in Oviedo and the CDMA Workshop on DSGE models in St. Andrews for their helpful comments. I would also like to thank Olaf Posch, Bent Jesper Christensen, Jesús Fernández-Villaverde, Kenneth Judd, Martin Møller Andreasen, Willi Semmler and seminar participants at CREATES for useful suggestions and insights. CREATES (Center for Research in Econometric Analysis of Time Series) is funded by the Danish National Research Foundation.

References

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