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Measurement with Minimal Theory

Author

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  • Ellen McGrattan

    (Research Federal Reserve Bank of Mpls)

Abstract

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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    Cited by:

    1. Victor Bystrov, 2020. "Identification and Estimation of Initial Conditions in Non-Minimal State-Space Models," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(4), pages 413-429, December.
    2. Fève, Patrick & Beaudry, Paul & Collard, Fabrice & Guay, Alain & Portier, Franck, 2022. "Dynamic Identification in VARs," TSE Working Papers 22-1384, Toulouse School of Economics (TSE).
    3. Charles Olivier Mao Takongmo, 2021. "DSGE models, detrending, and the method of moments," Bulletin of Economic Research, Wiley Blackwell, vol. 73(1), pages 67-99, January.
    4. Canova, Fabio, 2014. "Bridging DSGE models and the raw data," Journal of Monetary Economics, Elsevier, vol. 67(C), pages 1-15.
    5. Gianluca, MORETTI & Giulio, NICOLETTI, 2008. "Estimating DGSE models with long memory dynamics," Discussion Papers (ECON - Département des Sciences Economiques) 2008037, Université catholique de Louvain, Département des Sciences Economiques.
    6. Gianluca Moretti & Giulio Nicoletti, 2010. "Estimating DSGE models with unknown data persistence," Temi di discussione (Economic working papers) 750, Bank of Italy, Economic Research and International Relations Area.
    7. 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

    Keywords

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    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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