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Measurement with Minimal Theory

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Author Info
Ellen McGrattan () (Research Federal Reserve Bank of Mpls)

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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 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.

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Publisher Info
Paper provided by Society for Economic Dynamics in its series 2006 Meeting Papers with number 338.

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Date of creation: 03 Dec 2006
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Handle: RePEc:red:sed006:338

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Related research
Keywords: time series; business cycles;

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Find related papers by JEL classification:
E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General

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. Harald Uhlig, 1998. "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes 123, Quantitative Macroeconomics & Real Business Cycles. [Downloadable!]
    Other versions:
  2. 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)
  3. 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!]
    Other versions:
  4. V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2006. "Business cycle accounting," Staff Report 328, Federal Reserve Bank of Minneapolis. [Downloadable!]
    Other versions:
  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:
(explanations, 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. Christian Kascha & Karel Mertens, 2006. "Business Cycle Analysis and VARMA models," Economics Working Papers ECO2006/37, European University Institute. [Downloadable!]
    Other versions:
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