Measurement with minimal theory

Ellen McGrattan

Quarterly Review, 2010, issue July, No v. 33, No. 1, 2-13

Abstract: Applied macroeconomists interested in identifying the sources of business cycle fluctuations typically have no more than 40 or 50 years of data at a quarterly frequency. With sample sizes that small, identifi cation may not be possible even with correctly specifi ed representations of the data. In this article, I investigate whether small samples are indeed a problem for some commonly used statistical representations. I compare three?a vector autoregressive moving average (VARMA), an unrestricted state space, and a restricted state space?that are all consistent with the same prototype business cycle model. The statistical representations that I consider differ in the amount of a priori theory that is imposed, but all are correctly specifi ed. I fi nd that the identifying assumptions of VARMAs and unrestricted state space representations are too minimal: the range of estimates for statistics of interest for business cycle researchers is so large as to be uninformative.

Date: 2010
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Citations: View citations in EconPapers (7)

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Related works:
Working Paper: Measurement with minimal theory (2006)
Working Paper: Measurement with Minimal Theory (2006)
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedmqr:y:2010:i:july:p:2-13:n:v.33no.1

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