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Small-Sample Properties of GMM for Business-Cycle Analysis

  • Chistiano, Lawrence J
  • den Haan, Wouter J

The authors investigate, by Monte Carlo methods, the finite sample properties of generalized method of moments 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. The authors' 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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Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 14 (1996)
Issue (Month): 3 (July)
Pages: 309-27

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Handle: RePEc:bes:jnlbes:v:14:y:1996:i:3:p:309-27
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  7. Burnside, Craig & Eichenbaum, Martin & Rebelo, Sergio, 1993. "Labor Hoarding and the Business Cycle," Journal of Political Economy, University of Chicago Press, vol. 101(2), pages 245-73, April.
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  17. Prescott, Edward C., 1986. "Theory ahead of business-cycle measurement," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 25(1), pages 11-44, January.
  18. Cogley, Timothy & Nason, James M., 1995. "Effects of the Hodrick-Prescott filter on trend and difference stationary time series Implications for business cycle research," Journal of Economic Dynamics and Control, Elsevier, vol. 19(1-2), pages 253-278.
  19. Wouter J. Den Haan & Albert Marcet, 1994. "Accuracy in Simulations," Review of Economic Studies, Oxford University Press, vol. 61(1), pages 3-17.
  20. Jeffrey C. Fuhrer & George R. Moore & Scott Schuh, 1993. "Estimating the linear-quadratic inventory model: maximum likelihood versus generalized method of moments," Finance and Economics Discussion Series 93-11, Board of Governors of the Federal Reserve System (U.S.).
  21. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
  22. Lawrence J. Christiano & Martin Eichenbaum, 1990. "Current real business cycle theories and aggregate labor market fluctuations," Discussion Paper / Institute for Empirical Macroeconomics 24, Federal Reserve Bank of Minneapolis.
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  26. Anton Braun, R., 1994. "Tax disturbances and real economic activity in the postwar United States," Journal of Monetary Economics, Elsevier, vol. 33(3), pages 441-462, June.
  27. Donald W.K. Andrews & Christopher J. Monahan, 1990. "An Improved Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimator," Cowles Foundation Discussion Papers 942, Cowles Foundation for Research in Economics, Yale University.
  28. Ogaki, M., 1992. "An Introduction to the Generalized Method of Moments," RCER Working Papers 314, University of Rochester - Center for Economic Research (RCER).
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  34. Christiano, Lawrence J., 1988. "Why does inventory investment fluctuate so much?," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 247-280.
  35. Ericsson, Neil R, 1991. "Monte Carlo Methodology and the Finite Sample Properties of Instrumental Variables Statistics for Testing Nested and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 59(5), pages 1249-77, September.
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  39. Marshall, David A, 1992. " Inflation and Asset Returns in a Monetary Economy," Journal of Finance, American Finance Association, vol. 47(4), pages 1315-42, September.
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