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Large shocks, small shocks, and economic fluctuations: outliers in macroeconomic times series

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  • Nathan S. Balke
  • Thomas B. Fomby

Abstract

We analyse fifteen post-World War II US macroeconomic time series using a modified outlier identification procedure based on Tsay (1988a). "Large shocks" appear to be present in all the series we examined. Furthermore, there are three basic outlier patterns: (1) outliers seem to be associated with business cycles, (2) outliers are clustered together--both over time and across series, (3) there appears to be a dichotomy between outlier behaviour of real versus nominal series. Also, after controlling for outliers, much of the evidence of non-linearity in many of the time series is eliminated. Copyright 1994 by John Wiley & Sons, Ltd.
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Suggested Citation

  • Nathan S. Balke & Thomas B. Fomby, 1991. "Large shocks, small shocks, and economic fluctuations: outliers in macroeconomic times series," Working Papers 9101, Federal Reserve Bank of Dallas.
    • Handle: RePEc:fip:feddwp:9101
    Note: Published as: Balke, Nathan S. and Thomas B. Fomby (1994), "Large Shocks, Small Shocks, and Economic Fluctuations: Outliers in Macroeconomic Times Series," Journal of Applied Econometrics 9 (2): 181-200.
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    References listed on IDEAS

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    1. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    2. Scheinkman, Jose A & LeBaron, Blake, 1989. "Nonlinear Dynamics and Stock Returns," The Journal of Business, University of Chicago Press, vol. 62(3), pages 311-337, July.
    3. Hinich, Melvin J & Patterson, Douglas M, 1985. "Evidence of Nonlinearity in Daily Stock Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(1), pages 69-77, January.
    4. Brock, William A. & Sayers, Chera L., 1988. "Is the business cycle characterized by deterministic chaos?," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 71-90, July.
    5. Chen, Chung & Tiao, George C, 1990. "Random Level-Shift Time Series Models, ARIMA Approximations, and Level-Shift Detection," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 83-97, January.
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