Analysing DSGE Models with Global Sensitivity Analysis

Abstract

We present computational tools to analyse some key properties of DSGE models and address the following questions: (i) Which is the domain of structural coefficients assuring the stability and determinacy of a DSGE model? (ii) Which parameters mostly drive the fit of, e.g., GDP and which the fit of inflation? Is there any conflict between the optimal fit of one observed series versus another one? (iii) How to represent in a direct, albeit approximated, form the relationship between structural parameters and the reduced form of a rational expectations model? Global sensitivity analysis (GSA) techniques are used to answer these questions. We will discuss two classes of methods: Monte Carlo filtering (MCF) techniques and functional/variance decomposition techniques. These tools can make the model properties more transparent; helping the analyst to identify critical elements in the specification and, if necessary, guiding her to revise the model; supporting calibration and estimation procedures and interpreting estimation results. Applications to small DSGE models will complete the description of the methodologies.

This is a preview of subscription content, access via your institution.

References

  1. Adolfson, M., Lasen, S., Lind, J., & Villani, M. (2005). Bayesian estimation of an open economy DSGE model with incomplete pass-through. Working Paper Series 179, Sveriges Riksbank (Central Bank of Sweden), March 2005. available at http://ideas.repec.org/p/hhs/rbnkwp/0179.html.

  2. Benhabib J., Schmitt-Grohe S., Uribe M. (2003) Backward-looking interest-rate rules, interest-rate smoothing, and macroeconomic instability. Journal of Money, Credit and Banking 35: 1379–1412

    Article  Google Scholar 

  3. Coenen, G., Straub, R. (2005). Non-Ricardian households and fiscal policy in an estimated DSGE model of the Euro Area. Computing in economics and finance 2005, Society for Computational Economics, 2005. available at http://ideas.repec.org/p/sce/scecf5/102.html.

  4. De Fiore F., Liu Z. (2005) Does trade openness matter for aggregate instability?. Journal of Economic Dynamics and Control 7: 1165–1192

    Article  Google Scholar 

  5. Evans G.W., McGough B. (2005) Monetary policy and stable indeterminacy with inertia. Economics Letters 87: 1–7

    Article  Google Scholar 

  6. Hastie, T. J., & Tibshirani, R. J. (1996). Generalized additive models. Chapman and Hall.

  7. Hornberger G.M., Spear R.C. (1981) An approach to the preliminary analysis of environmental systems. Journal of Environmental Management 7: 7–18

    Google Scholar 

  8. Ireland P. (2004) A method for taking models to the data. Journal of Economic Dynamics and Control 28: 1205–1226

    Article  Google Scholar 

  9. Judd, K. L. (1998). Numerical methods in economics. The MIT press.

  10. Juillard, M. (2004). DYNARE: A program for simulating and estimating SDGE models, 2004. http://www.cepremap.cnrs.fr/dynare.

  11. Juillard, M., Karam, P., Laxton, D., & Pesenti, P. (2006). Welfare-based monetary policy rules in an estimated DSGE model of the US economy. Working Paper Series 613, European Central Bank, April 2006.

  12. Kucherenko, S., & Mauntz, W. (2007). Applicaton of global sensitivity indices for measuring the effectiveness of Quasi-Monte Carlo methods. Monte Carlo Methods and Simulation, (in press).

  13. Kuttner K.N. (1994) Estimating potential output as a latent variable. Journal of Business and Economic Statistics 12: 361–368

    Article  Google Scholar 

  14. Levin A., Wieland W., Williams J.C. (2003). The performance of forecast-based monetary policy rules under model uncertainty. American Economic Review 93: 622–645

    Article  Google Scholar 

  15. Li G., Wang S.W., Rabitz H. (2002). Practical approaches to construct RS-HDMR component functions. Journal of Physical Chemistry 106: 8721–8733

    Google Scholar 

  16. Li G., Hu J., Wang S.-W., Georgopoulos P.G., Schoendorf J., Rabitz H. (2006). Random Sampling-High Dimensional Model Representation (RS-HDMR) and orthogonality of its different order component functions. Journal of Physical Chemistry A 110: 2474–2485

    Article  Google Scholar 

  17. Lubik, T. (2003). Investment spending, equilibrium indeterminacy, and the interactions of monetary and fiscal policy. Technical Report Economics Working Paper Archive 490, The Johns Hopkins University.

  18. Lubik, T., & Schorfheide, F. (2007). Do central banks respond to exchange rate movements?. A structural investigation. Journal of Monetary Economics, doi: 10.1016/j.jmoneco.2006.01.009

    Google Scholar 

  19. Lubik, T., & Schorfheide, F. (2005). A Bayesian look at new open economy macroeconomics. Economics Working Paper Archive 521, The Johns Hopkins University,Department of Economics, May 2005. available at http://ideas.repec.org/p/jhu/papers/521.html.

  20. Oakley J., O’Hagan A. (2004) Probabilistic sensitivity analysis of complex models: a Bayesian approach. Journal of Royal Statistic Society B 66: 751–769

    Article  Google Scholar 

  21. Pytlarczyk, E. (2005). An estimated DSGE model for the German economy within the Euro Area. Discussion Paper Series 1: Economic Studies 2005,33, Deutsche Bundesbank, Research Centre, 2005. available at http://ideas.repec.org/p/zbw/bubdp1/4227.html.

  22. Rabitz, H., & Aliş, Ö. F. (2000). Managing the tyranny of parameters in mathematical modelling of physical systems. In A. Saltelli, K. Chan, & M. Scott (Ed.), Sensitivity analysis (pp. 199–223). John Wiley and Sons Publishers.

  23. Rabitz H., Aliş Ö.F., Shorter J., Shim K. (1999) Efficient input-output model representations. Computer Physics Communications 117: 11–20

    Article  Google Scholar 

  24. Ratto, M., Tarantola, S., Saltelli, A., & Young, P. C. (2004). Accelerated estimation of sensitivity indices using state dependent parameter models. In K. M. Hanson & F. M. Hemez (Eds.), Sensitivity analysis of model output, Proceedings of the 4th International Conference on Sensitivity Analysis of Model Output (SAMO 2004) Santa Fe, New Mexico, March 8–11, 2004 (pp. 61–70), 2004. http://library.lanl.gov/ccw/samo2004/.

  25. Ratto, M., Roeger, W., in ’t Veld, J., & Girardi, R. (2005a). An estimated open-economy model for the EURO Area. Technical Report EUR 21882 EN, Joint Research Centre, European Commission.

  26. Ratto, M., Roeger, W., in ’t Veld, J., & Girardi, R. (2005b). An estimated new Keynesian dynamic stochastic general equilibrium model of the Euro area. Economic Papers. No. 220 EUR 21882 EN, European Commission, January 2005.

  27. Ratto, M., Roeger, W., & in ’t Veld, J. (2006). Fiscal policy in an estimated open-economy model for the EURO area. Computing in economics and finance 2006, Society for Computational Economics, 2006. available at http://ideas.repec.org/p/sce/scecfa/43.html.

  28. Ratto M., Pagano A., Young P.C. (2007). State dependent parameter metamodelling and sensitivity analysis. Computer Physics Communications, doi:10.1016/j.cpc.2007.07.011

    Google Scholar 

  29. Roeger, W., & in ’t Veld, J. (1997). Quest II. A multi-country business cycle and growth model. Economic Papers. No. 123 II/511/97-EN, European Commission, Brussels, October 1997.

  30. Saltelli, A., Tarantola, S., Campolongo, F., & Ratto, M. (2004). Sensitivity analysis in practice: A guide to assessing scientific models. Wiley.

  31. Schorfheide F. (2000). Loss function-based evaluation of DSGE models. Journal of Applied Econometrics 15(6): 645–670

    Article  Google Scholar 

  32. Sims C. (2002). Solving rational expectations models. Computational Economics 20, 1–20

    Google Scholar 

  33. Smets F., Wouters R. (2003). An estimated dynamic stochastic general equilibrium model of the Euro Area. Journal of the European Economic Association 1(5): 1123–1175

    Article  Google Scholar 

  34. Sobol’ I.M. (1976) Uniformly distributed sequences with additional uniformity properties. USSR Computational MAthematics and Mathematical Physics 16(5): 236–242

    Article  Google Scholar 

  35. Sobol’, I. M. (1990). Sensitivity estimates for nonlinear mathematical models. Matematicheskoe Modelirovanie, 2, 112–118 in Russian, translated in English in Sobol93.

  36. Sobol’, I. M. (1993). Sensitivity analysis for non-linear mathematical models. Mathematical Modelling and Computational Experiment, 1, 407–414. English translation of Russian original paper Sobol9093.

  37. Sobol’ I.M. (1998) On quasi-Monte Carlo integrations. Mathematics and Computers in Simulation 47, 103–112

    Article  Google Scholar 

  38. Sobol’, I. M., Turchaninov, V. I., Levitan, Yu. L., & Shukhman, B. V. (1992). Quasirandom sequence generators. Ipm zak. no.30, Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow.

  39. Young P.C. (1978) A general theory of modelling for badly defined dynamic systems. In: Vansteenkiste G.C. (eds). Modeling, identification and control in environmental systems. North Holland, Amsterdam, pp. 103–135

    Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Marco Ratto.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Ratto, M. Analysing DSGE Models with Global Sensitivity Analysis. Comput Econ 31, 115–139 (2008). https://doi.org/10.1007/s10614-007-9110-6

Download citation

Keywords

  • Stability mapping
  • Reduced form solution
  • DSGE models
  • Monte Carlo filtering
  • Global sensitivity analysis
  • High dimensional model representation

JEL Classification

  • C02
  • C60
  • C62
  • C63