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Frequency Domain Analysis of Medium Scale DSGE Models with Application to Smets and Wouters (2007)

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Author Info

  • Zhongjun Qu

    ()
    (Department of Economics, Boston University)

  • Denis Tkachenko

    ()
    (Department of Economics, Boston University)

Abstract

The paper considers parameter identification, estimation and inference in medium scale DSGE models from a frequency domain perspective using the framework developed in Qu and Tkachenko (2010). The analysis uses Smets and Wouters (2007) as an illustrative example, motivated by the fact that it has become a workhorse model in the DSGE literature. For identification, in additional to checking parameter identifiably, we derive the non-identification curve to depict parameter values that yield observational equivalence, revealing which and how many parameters need to be fixed to achieve local identification. For estimation and inference, we contrast estimates obtained using the full spectrum with those using only the business cycle frequencies to find notably di¤erent parameter values and impulse response functions. A further comparison between the nonparametrically estimated and model implied spectra suggests that the business cycle based method delivers better estimates of the features that the model is intended to capture. Overall, the results suggest that the frequency domain based approach, in part due to its ability in handling subsets of frequencies, constitutes a fiexible framework for studying medium scale DSGE models.

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Bibliographic Info

Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number WP2011-060.

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Length: 64 pages
Date of creation: Jan 2011
Date of revision:
Handle: RePEc:bos:wpaper:wp2011-060

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Related research

Keywords: Dynamic stochastic general equilibrium models; frequency domain; identification; MCMC; model diagnostics; spectrum;

References

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  1. Chris Otrok, 1999. "On Measuring the Welfare Cost of Business Cycles," Virginia Economics Online Papers 318, University of Virginia, Department of Economics.
  2. Nikolay Iskrev, 2009. "Local Identification in DSGE Models," Working Papers w200907, Banco de Portugal, Economics and Research Department.
  3. Eric M. Leeper & Christopher A. Sims, 1994. "Toward a Modern Macroeconomic Model Usable for Policy Analysis," NBER Chapters, in: NBER Macroeconomics Annual 1994, Volume 9, pages 81-140 National Bureau of Economic Research, Inc.
  4. Christopher A. Sims, 1990. "Rational expectations modeling with seasonally adjusted data," Discussion Paper / Institute for Empirical Macroeconomics 35, Federal Reserve Bank of Minneapolis.
  5. Frank Schorfheide, 2011. "Estimation and evaluation of DSGE models: progress and challenges," Working Papers 11-7, Federal Reserve Bank of Philadelphia.
  6. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  7. Zhongjun Qu & Denis Tkachenko, 2010. "Identification and Frequency Domain QML Estimation of Linearized DSGE Models," Boston University - Department of Economics - Working Papers Series WP2010-053, Boston University - Department of Economics.
  8. Hansen, Lars Peter & Sargent, Thomas J., 1993. "Seasonality and approximation errors in rational expectations models," Journal of Econometrics, Elsevier, vol. 55(1-2), pages 21-55.
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