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Small sample properties of GMM for business cycle analysis Author info | Abstract | Publisher info | Download info | Related research | Statistics Lawrence J. Christiano
Wouter Den Haan
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We investigate, by Monte Carlo methods, the finite sample properties of GMM procedures for conducting inference about statistics that are of interest in the business cycle literature. These statistics include the second moments of data filtered using the first difference and Hodrick-Prescott filters, and they include statistics for evaluating model fit. Our results indicate that, for the procedures considered, the existing asymptotic theory is not a good guide in a sample the size of quarterly postwar U.S. data.
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Date of creation: 1995Date of revision:
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Keywords: Business cycles ; Other versions of this item:
Article Paper Lawrence J. Christiano & Wouter J. Den Haan, 1995.
"Small Sample Properties of GMM for Business Cycle Analysis ,"
NBER Technical Working Papers
0177, National Bureau of Economic Research, Inc.
[Downloadable!] (restricted) Lawrence J. Christiano & Wouter den Haan, 1995.
"Small sample properties of GMM for business cycle analysis ,"
Working Paper Series, Macroeconomic Issues
95-3, Federal Reserve Bank of Chicago.
Lawrence J. Christiano & Wouter den Haan, 1994.
"Small Sample Properties of GMM for Business Cycle Analysis ,"
University of California at San Diego, Economics Working Paper Series
94-17, Department of Economics, UC San Diego.
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"Inferences from Parametric and Non-Parametric Covariance Matrix Estimation Procedures ,"
University of California at San Diego, Economics Working Paper Series
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"Effects of the Hodrick-Prescott filter on trend and difference stationary time series: implications for business cycle research ,"
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