Measurement with Minimal Theory
AbstractA 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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Bibliographic InfoPaper provided by Society for Economic Dynamics in its series 2006 Meeting Papers with number 338.
Date of creation: 03 Dec 2006
Date of revision:
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Postal: Society for Economic Dynamics Christian Zimmermann Economic Research Federal Reserve Bank of St. Louis PO Box 442 St. Louis MO 63166-0442 USA
Web page: http://www.EconomicDynamics.org/society.htm
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time series; business cycles;
Other versions of this item:
- 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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- 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, 2008. "Are Structural VARs with Long-Run Restrictions Useful in Developing Business Cycle Theory?," NBER Working Papers 14430, National Bureau of Economic Research, Inc.
- 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, 2002.
"Business cycle accounting,"
625, Federal Reserve Bank of Minneapolis.
- 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, 2006. "Business cycle accounting," Staff Report 328, Federal Reserve Bank of Minneapolis.
- V V Chari & Patrick J Kehoe & Ellen R. McGrattan, 2003. "Business Cycle Accounting," Levine's Bibliography 506439000000000421, UCLA Department of Economics.
- V. V. Chari & Patrick Kehoe & Ellen McGrattan, 2004. "Business Cycle Accounting," Levine's Bibliography 122247000000000560, UCLA Department of Economics.
- 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.
- 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.
- Uhlig, H., 1995. "A toolkit for analyzing nonlinear dynamic stochastic models easily," Discussion Paper 1995-97, Tilburg University, Center for Economic Research.
- Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-23, July.
- Christian Kascha & Karel Mertens, 2006.
"Business Cycle Analysis and VARMA models,"
Economics Working Papers
ECO2006/37, European University Institute.
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