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Small sample properties of GMM for business cycle analysis

  • Lawrence J. Christiano
  • Wouter Den Haan

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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Paper provided by Federal Reserve Bank of Minneapolis in its series Staff Report with number 199.

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Date of creation: 1995
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Handle: RePEc:fip:fedmsr:199
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  1. Edward C. Prescott, 1986. "Theory ahead of business cycle measurement," Staff Report 102, Federal Reserve Bank of Minneapolis.
  2. Timothy Cogley & James M. Nason, 1993. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series: implications for business cycle research," Working Papers in Applied Economic Theory 93-01, Federal Reserve Bank of San Francisco.
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  7. den Haan, Wouter J., 1995. "The term structure of interest rates in real and monetary economies," Journal of Economic Dynamics and Control, Elsevier, vol. 19(5-7), pages 909-940.
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  15. Newey, Whitney K & West, Kenneth D, 1987. "A Simple, Positive Semi-definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix," Econometrica, Econometric Society, vol. 55(3), pages 703-08, May.
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  30. Tauchen, George, 1986. "Statistical Properties of Generalized Method-of-Moments Estimators of Structural Parameters Obtained from Financial Market Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(4), pages 397-416, October.
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