Open Economies Review

, Volume 20, Issue 4, pp 435–471

Testing a DSGE Model of the EU Using Indirect Inference

Research Article

Abstract

We use the method of indirect inference, using the bootstrap, to test the Smets and Wouters model of the EU against a VAR auxiliary equation describing their data. We find that their model generates excessive variance compared with the data. But their model fits the dynamic facts quite well if the errors have the properties assumed by SW but scaled down. We compare a New Classical version of the model which also performs reasonably if error properties are chosen using New Classical priors (notably excluding shocks to preferences). Both versions have (different) difficulties fitting the data if the actual error properties are used. A model combining rigid and flexible-wage/price sectors, with a weight of around 5% on the rigid sector, does best in fitting the data.

Keywords

Bootstrap DSGE model VAR model Model of EU Indirect inference Wald statistic 

JEL Classification

C12 C32 

References

  1. Annexes (2009) Supporting annexes to this paper. Available at www.cf.ac.uk/carbs/faculty/minfordp/SWEUAnnex.pdf
  2. Canova F (1994) Statistical inference in calibrated models. J Appl Econ 9:S123–S144CrossRefGoogle Scholar
  3. Canova F (2005) Methods for applied macroeconomic research. Princeton University Press, PrincetonGoogle Scholar
  4. Canzoneri MB, Cumby RE, Diba BT (2007) Euler equations and money market interest rates: a challenge for monetary policy models. J Monet Econ 54:1863–1881CrossRefGoogle Scholar
  5. Christiano L, Eichenbaum M, Evans C (2005) Nominal rigidities and the dynamic effects of a shock to monetary policy. J Polit Econ 113:1–45CrossRefGoogle Scholar
  6. Evans GW, Honkapohja S (2005) An interview with Thomas J. Sargent. Macroecon Dyn 9:561–583CrossRefGoogle Scholar
  7. Gourieroux C, Monfort A (1995) Simulation based econometric methods. CORE Lectures Series, Louvain-la-NeuveGoogle Scholar
  8. Gourieroux C, Monfort A, Renault E (1993) Indirect inference. J Appl Econ 8:85–118CrossRefGoogle Scholar
  9. Gregory A, Smith G (1991) Calibration as testing: inference in simulated macro models. J Bus Econ Stat 9:293–303Google Scholar
  10. Gregory A, Smith G (1993) Calibration in macroeconomics. In: Maddala G (ed) Handbook of statistics, vol 11. Elsevier, St. Louis, pp 703–719Google Scholar
  11. Juillard M (2001) Dynare: a program for the simulation of rational expectations models. Computing in economics and finance 213. Society for Computational EconomicsGoogle Scholar
  12. Lucas R (1972) Expectations and the neutrality of money. J Econ Theory 4:103–124CrossRefGoogle Scholar
  13. Mankiw G, Reis R (2002) Sticky information versus sticky prices: a proposal to replace the new Keynesian Phillips curve. Q J Econ 117:1295–1328CrossRefGoogle Scholar
  14. Sims C (2003) Implications of rational inattention. J Monet Econ 50:665–690Google Scholar
  15. Smets F, Wouters R (2003) An estimated stochastic dsge model of the euro area. J Eur Econ Assoc 1:1123–1175CrossRefGoogle Scholar
  16. Smith AA (1993) Estimating nonlinear time-series models using simulated vector autoregressions. J Appl Econ 8:S63–S84CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • David Meenagh
    • 1
  • Patrick Minford
    • 2
    • 3
  • Michael Wickens
    • 1
    • 3
    • 4
  1. 1.Cardiff Business SchoolCardiff UniversityCardiffUK
  2. 2.Cardiff Business SchoolCardiff UniversityCardiffUK
  3. 3.CEPRLondonUK
  4. 4.Department of Economics and Related StudiesUniversity of YorkYorkUK

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