Measurement with minimal theory
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.
|Date of creation:||2006|
|Date of revision:|
|Contact details of provider:|| Postal: 90 Hennepin Avenue, P.O. Box 291, Minneapolis, MN 55480-0291|
Phone: (612) 204-5000
Web page: http://minneapolisfed.org/
More information through EDIRC
|Order Information:|| Web: http://www.minneapolisfed.org/pubs/ Email: |
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.:
- V V Chari & Patrick J Kehoe & Ellen R. McGrattan, 2003.
"Business Cycle Accounting,"
506439000000000421, UCLA Department of Economics.
- V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2006. "Business cycle accounting," Staff Report 328, Federal Reserve Bank of Minneapolis.
- V. V. Chari & Patrick Kehoe & Ellen McGrattan, 2004. "Business Cycle Accounting," Levine's Bibliography 122247000000000560, UCLA Department of Economics.
- V.V. Chari & Patrick J. Kehoe & Ellen McGrattan, 2004. "Business Cycle Accounting," NBER Working Papers 10351, National Bureau of Economic Research, Inc.
- V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2002. "Business cycle accounting," Working Papers 625, Federal Reserve Bank of Minneapolis.
- Uhlig, H.F.H.V.S., 1995.
"A toolkit for analyzing nonlinear dynamic stochastic models easily,"
1995-97, Tilburg University, Center for Economic Research.
- Harald Uhlig, 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper / Institute for Empirical Macroeconomics 101, Federal Reserve Bank of Minneapolis.
- Harald Uhlig, 1998. "A Toolkit for Analysing Nonlinear Dynamic Stochastic Models Easily," QM&RBC Codes 123, Quantitative Macroeconomics & Real Business Cycles.
- Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-23, July.
- 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.
- 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.
- V. V. Chari & Patrick J. Kehoe & Ellen R. McGrattan, 2008. "Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory?," NBER Working Papers 14430, National Bureau of Economic Research, Inc.
- 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.
When requesting a correction, please mention this item's handle: RePEc:fip:fedmwp:643. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Janelle Ruswick)
If references are entirely missing, you can add them using this form.