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Measurement with Minimal Theory


  • Ellen McGrattan

    () (Research Federal Reserve Bank of Mpls)


A central debate in applied macroeconomics is whether statistical tools that use minimal identifying assumptions are useful for isolating promising models within a broad class. In this paper, I extend the analysis of Chari, Kehoe, and McGrattan (2005) to compare four statistical methods---structural VARs, VARMAs, unrestricted state space methods, and restricted state space methods---all applied to data from the same business cycle model. The objective is to determine which, if any, of the methods can successfully uncover moments of the underlying economy. The methods differ in the amount of a priori theory that is imposed, with structural VARs imposing minimal assumptions and restricted state space methods imposing the maximal. The moments that I focus on are those typically reported in the business cycle literature. Preliminary results show that the identifying assumptions of structural VARs, VARMAs, and unrestricted state space methods are too minimal: they cannot robustly uncover many of the moments business cycle researchers are interested in measuring.

Suggested Citation

  • Ellen McGrattan, 2006. "Measurement with Minimal Theory," 2006 Meeting Papers 338, Society for Economic Dynamics.
  • Handle: RePEc:red:sed006:338

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    References listed on IDEAS

    1. Uhlig, H.F.H.V.S., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
    2. Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-723, July.
    3. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Business Cycle Accounting," Econometrica, Econometric Society, vol. 75(3), pages 781-836, May.
    4. Chari, V.V. & Kehoe, Patrick J. & McGrattan, Ellen R., 2008. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Journal of Monetary Economics, Elsevier, vol. 55(8), pages 1337-1352, November.
    5. Burmeister, Edwin & Wall, Kent D & Hamilton, James D, 1986. "Estimation of Unobserved Expected Monthly Inflation Using Kalman Filtering," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(2), pages 147-160, April.
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    Cited by:

    1. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.
    2. Kascha, Christian & Mertens, Karel, 2009. "Business cycle analysis and VARMA models," Journal of Economic Dynamics and Control, Elsevier, vol. 33(2), pages 267-282, February.

    More about this item


    time series; business cycles;

    JEL classification:

    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General


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