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Identification and Frequency Domain QML Estimation of Linearized DSGE Models

  • Zhongjun Qu

    ()

    (Department of Economics, Boston University)

  • Denis Tkachenko

    ()

    (Department of Economics, Boston University)

This paper considers issues related to identification, inference and computation in linearized Dynamic Stochastic General Equilibrium (DSGE) models. We first provide a necessary and su¢ cient condition for the local identification of the structural parameters based on the (first and) second order properties of the process. The condition allows for arbitrary relations be- tween the number of observed endogenous variables and structural shocks and is simple to verify. The extensions, including identification through a subset of frequencies, partial iden- tification, conditional identification and identification under general nonlinear constraints, are also studied. When lack of identification is detected, the method can be further used to trace out non-identification curves. For estimation, restricting our attention to nonsingular systems, we consider a frequency domain quasi-maximum likelihood (FQML) estimator and present its asymptotic properties. The limiting distribution of the estimator can be di¤erent from results in the related literature due to the structure of the DSGE model. Finally, we discuss a quasi- Bayesian procedure for estimation and inference. The procedure can be used to incorporate relevant prior distributions and is computationally attractive.

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Paper provided by Boston University - Department of Economics in its series Boston University - Department of Economics - Working Papers Series with number WP2010-053.

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Length: 55 pages
Date of creation: Jan 2010
Date of revision:
Handle: RePEc:bos:wpaper:wp2010-053
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  1. Consolo, Agostino & Favero, Carlo A. & Paccagnini, Alessia, 2009. "On the Statistical Identification of DSGE Models," CEPR Discussion Papers 7176, C.E.P.R. Discussion Papers.
  2. Sumru Altug, 1986. "Time to build and aggregate fluctuations: some new evidence," Working Papers 277, Federal Reserve Bank of Minneapolis.
  3. 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.
  4. Lawrence J. Christiano & Robert J. Vigfusson, 2001. "Maximum likelihood in the frequency domain: the importance of time-to-plan," Working Paper 0106, Federal Reserve Bank of Cleveland.
  5. Chernozhukov, Victor & Hong, Han, 2003. "An MCMC approach to classical estimation," Journal of Econometrics, Elsevier, vol. 115(2), pages 293-346, August.
  6. Nikolay Iskrev, 2009. "Local Identification in DSGE Models," Working Papers w200907, Banco de Portugal, Economics and Research Department.
  7. Berkowitz, Jeremy, 2001. "Generalized spectral estimation of the consumption-based asset pricing model," Journal of Econometrics, Elsevier, vol. 104(2), pages 269-288, September.
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