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Global sensitivity analysis for macro-economic models

Author

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  • Marco Ratto

    (European commission, Joint Research Centre)

Abstract

DSGE models are customarily built in the presence of uncertainties of various levels, such as the specification of behavioural equations of economic agents, the actual values of model parameters, and so on. When the degree of complexity of the model structure and its parameterization increases, it becomes not trivial if not impossible to know a priory the set of model coefficients assuring the stability of a model, or the mapping between structural parameters and the reduced form of a rational expectations model. Global sensitivity analysis techniques can be very useful in this context, helping to make the model structure and properties more transparent to the analyst. In this paper we will discuss two classes of methods: Monte Carlo Filtering techniques and functional/variance decomposition techniques. Monte Carlo filtering (MCF) techniques can be used to map the stability region of DSGE models and to detect parameters that mostly drive the violation of the rank condition. Such procedure is extremely useful for detecting critical regions in the model parameter space of DSGE models. In addition to stability, MCF techniques are also useful to map the fit of each singular series in complex multivariate systems, to answer the following types of questions: which parameters mostly drive the fit of GDP and which the fit of inflation? Is there any trade-off? The second class of sensitivity techniques is based on the so-called High-Dimensional Model Representation. Such a functional decomposition can be very effective in giving a non-parametric representation of the input-output mapping. For example, this approach can be used to map the relationship between structural parameters and the reduced form of rational expectation models. Applications to small DSGE models will complement the description of the methodologies.

Suggested Citation

  • Marco Ratto, 2006. "Global sensitivity analysis for macro-economic models," Computing in Economics and Finance 2006 42, Society for Computational Economics.
  • Handle: RePEc:sce:scecfa:42
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    File URL: http://www.jrc.cec.eu.int/uasa/doc/CEF2006/GSA_macro.pdf
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    References listed on IDEAS

    as
    1. Lubik, Thomas A. & Schorfheide, Frank, 2007. "Do central banks respond to exchange rate movements? A structural investigation," Journal of Monetary Economics, Elsevier, vol. 54(4), pages 1069-1087, May.
    2. Frank Schorfheide, 2000. "Loss function-based evaluation of DSGE models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 645-670.
    3. Kuttner, Kenneth N, 1994. "Estimating Potential Output as a Latent Variable," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 361-368, July.
    4. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Cited by:

    1. Riggi, Marianna & Tancioni, Massimiliano, 2010. "Nominal vs real wage rigidities in New Keynesian models with hiring costs: A Bayesian evaluation," Journal of Economic Dynamics and Control, Elsevier, vol. 34(7), pages 1305-1324, July.

    More about this item

    Keywords

    Sensitivity Analysis; DSGE models; Mapping stability;

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C62 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Existence and Stability Conditions of Equilibrium
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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