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:
Contact details of provider:
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
More information through EDIRC
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
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.:
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