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Generalized spectral estimation

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  • Jeremy Berkowitz

Abstract

This paper provides a framework for estimating parameters in a wide class of dynamic rational expectations models. The framework recognizes that RE models are often meant to match the data only in limited ways. In particular, interest may focus on a subset of frequencies. This paper designs a frequency domain version of GMM. The estimator has several advantages over traditional GMM. Aside from allowing band-restricted estimation, it does not require making arbitrary instrument or weighting matrix choices. The framework also includes least squares, maximum likelihood, and band restricted maximum likelihood as special cases.

Suggested Citation

  • Jeremy Berkowitz, 1996. "Generalized spectral estimation," Finance and Economics Discussion Series 96-37, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:96-37
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    References listed on IDEAS

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    1. Francis X. Diebold & Lee E. Ohanian & Jeremy Berkowitz, 1998. "Dynamic Equilibrium Economies: A Framework for Comparing Models and Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 433-451.
    2. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    3. Engle, Robert F, 1980. "Exact Maximum Likelihood Methods for Dynamic Regressions and Band Spectrum Regressions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 21(2), pages 391-407, June.
    4. Durlauf, Steven N., 1991. "Spectral based testing of the martingale hypothesis," Journal of Econometrics, Elsevier, vol. 50(3), pages 355-376, December.
    5. Wouter J. Den Haan & Albert Marcet, 1994. "Accuracy in Simulations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 61(1), pages 3-17.
    6. Taylor, John B & Uhlig, Harald, 1990. "Solving Nonlinear Stochastic Growth Models: A Comparison of Alternative Solution Methods," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 1-17, January.
    7. Granger, C. W. J. & Newbold, Paul, 1986. "Forecasting Economic Time Series," Elsevier Monographs, Elsevier, edition 2, number 9780122951831 edited by Shell, Karl.
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    Cited by:

    1. Hoover, Kevin D., 1997. "Real business-cycle realizations, 1925-1995 : A comment," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 47(1), pages 281-290, December.
    2. Jaromir Benes & Tibor Hledik & Michael Kumhof & David Vavra, 2005. "An Economy in Transition and DSGE: What the Czech National Bank’s New Projection Model Needs," Working Papers 2005/12, Czech National Bank.

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