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Small sample properties of GMM for business cycle analysis

Listed author(s):
  • Lawrence J. Christiano
  • Wouter den Haan

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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Paper provided by Federal Reserve Bank of Chicago in its series Working Paper Series, Macroeconomic Issues with number 95-3.

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Date of creation: 1995
Handle: RePEc:fip:fedhma:95-3
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  1. Lawrence J. Christiano & Martin Eichenbaum, 1990. "Current real business cycle theories and aggregate labor market fluctuations," Working Paper Series, Macroeconomic Issues 90, Federal Reserve Bank of Chicago.
  2. Kenneth D. West & David W. Wilcox, 1993. "Some evidence on finite sample behavior of an instrumental variables estimator of the linear quadratic inventory model," Finance and Economics Discussion Series 93-29, Board of Governors of the Federal Reserve System (U.S.).
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  5. 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.
  6. Craig Burnside & Martin Eichenbaum & Sergio Rebelo, 1990. "Labor Hoarding and the Business Cycle," NBER Working Papers 3556, National Bureau of Economic Research, Inc.
  7. Cooley, Thomas F & Hansen, Gary D, 1989. "The Inflation Tax in a Real Business Cycle Model," American Economic Review, American Economic Association, vol. 79(4), pages 733-748, September.
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  19. Donald W.K. Andrews, 1988. "Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation," Cowles Foundation Discussion Papers 877R, Cowles Foundation for Research in Economics, Yale University, revised Jul 1989.
  20. Wouter J. Den Haan & Andrew T. Levin, 1995. "Inferences from parametric and non-parametric covariance matrix estimation procedures," International Finance Discussion Papers 504, Board of Governors of the Federal Reserve System (U.S.).
  21. Fuhrer, Jeffrey C. & Moore, George R. & Schuh, Scott D., 1995. "Estimating the linear-quadratic inventory model Maximum likelihood versus generalized method of moments," Journal of Monetary Economics, Elsevier, vol. 35(1), pages 115-157, February.
  22. Craig Burnside & Martin Eichenbaum, 1994. "Small Sample Properties of Generalized Method of Moments Based Wald Tests," NBER Technical Working Papers 0155, National Bureau of Economic Research, Inc.
  23. 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.
  24. David Backus & Patrick Kehoe & Finn Kydland, 1992. "Dynamics of the trade balance and the terms of trade: the J-curve revisited," Discussion Paper / Institute for Empirical Macroeconomics 65, Federal Reserve Bank of Minneapolis.
  25. 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 1249-1277, September.
  26. Edward C. Prescott, 1986. "Theory ahead of business cycle measurement," Staff Report 102, Federal Reserve Bank of Minneapolis.
  27. Braun, R Anton & Evans, Charles L, 1998. "Seasonal Solow Residuals and Christmas: A Case for Labor Hoarding and Increasing Returns," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 30(3), pages 306-330, August.
  28. Davidson, Russell & MacKinnon, James G., 1993. "Estimation and Inference in Econometrics," OUP Catalogue, Oxford University Press, number 9780195060119.
  29. Kydland, Finn E & Prescott, Edward C, 1982. "Time to Build and Aggregate Fluctuations," Econometrica, Econometric Society, vol. 50(6), pages 1345-1370, November.
  30. 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.
  31. Whitney K. Newey & Kenneth D. West, 1986. "A Simple, Positive Semi-Definite, Heteroskedasticity and AutocorrelationConsistent Covariance Matrix," NBER Technical Working Papers 0055, National Bureau of Economic Research, Inc.
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  33. Nelson, C. & Startz, R., 1988. "The Distribution Of The Instrumental Variables Estimator And Its T-Ratio When The Instrument Is A Poor One," Working Papers 88-07, University of Washington, Department of Economics.
  34. 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.
  35. Fisher, Jonas D. M., 1997. "Relative prices, complementarities and comovement among components of aggregate expenditures," Journal of Monetary Economics, Elsevier, vol. 39(3), pages 449-474, August.
  36. Christiano, Lawrence J., 1988. "Why does inventory investment fluctuate so much?," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 247-280.
  37. Singleton, Kenneth J., 1988. "Econometric issues in the analysis of equilibrium business cycle models," Journal of Monetary Economics, Elsevier, vol. 21(2-3), pages 361-386.
  38. Long, John B, Jr & Plosser, Charles I, 1983. "Real Business Cycles," Journal of Political Economy, University of Chicago Press, vol. 91(1), pages 39-69, February.
  39. Kocherlakota, Narayana R., 1990. "On tests of representative consumer asset pricing models," Journal of Monetary Economics, Elsevier, vol. 26(2), pages 285-304, October.
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