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Measurement with minimal theory

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Author Info
Ellen R. McGrattan

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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 compare three statistical models?a vector autoregressive moving average (VARMA) model, an unrestricted state space model, and a restricted state space model?that are all consistent with the same prototype business cycle model. The business cycle model is a prototype in the sense that many models, with various frictions and shocks, are observationally equivalent to it. The statistical models I consider differ in the amount of a priori theory that is imposed, with VARMAs imposing minimal assumptions and restricted state space models imposing the maximal. The objective is to determine if it is possible to successfully uncover statistics of interest for business cycle theorists with sample sizes used in practice and only minimal identifying assumptions imposed. I find that the identifying assumptions of VARMAs and unrestricted state space models are too minimal: The range of estimates are so large as to be uninformative for most statistics that business cycle researchers need to distinguish alternative theories.

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Paper provided by Federal Reserve Bank of Minneapolis in its series Working Papers with number 643.

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Date of creation: 2006
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Handle: RePEc:fip:fedmwp:643

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Keywords: Business cycles - Econometric models

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This paper has been announced in the following NEP Reports: References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2007. "Are structural VARs with long-run restrictions useful in developing business cycle theory?," Staff Report 364, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  2. Harald Uhlig, 1998. "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes 123, Quantitative Macroeconomics & Real Business Cycles. [Downloadable!]
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  3. Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-23, July. [Downloadable!] (restricted)
  4. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2006. "Business cycle accounting," Staff Report 328, Federal Reserve Bank of Minneapolis. [Downloadable!]
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  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 147-60, April.
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Cited by:
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  1. Christian Kascha & Karel Mertens, 2006. "Business Cycle Analysis and VARMA models," Economics Working Papers ECO2006/37, European University Institute. [Downloadable!]
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