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Evidence on Structural Instability in Macroeconomic Time Series Relations

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  • Stock, James H
  • Watson, Mark W

Abstract

An experiment is performed to assess the prevalence of instability in univariate and bivariate macroeconomic time series relations and to ascertain whether various adaptive forecasting techniques successfully handle any such instability. Formal tests for instability and out-of-sample forecasts from sixteen different models are computed using a sample of seventy-six representative U.S. monthly postwar macroeconomic time series, constituting 5,700 bivariate forecasting relations. The tests for instability and the forecast comparisons suggest that there is substantial instability in a significant fraction of the univariate and bivariate autoregressive models.

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Bibliographic Info

Article provided by American Statistical Association in its journal Journal of Business and Economic Statistics.

Volume (Year): 14 (1996)
Issue (Month): 1 (January)
Pages: 11-30

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Handle: RePEc:bes:jnlbes:v:14:y:1996:i:1:p:11-30

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  12. Meese, Richard & Geweke, John, 1984. "A Comparison of Autoregressive Univariate Forecasting Procedures for Macroeconomic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(3), pages 191-200, July.
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  16. Chow, Gregory C., 1984. "Random and changing coefficient models," Handbook of Econometrics, in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 21, pages 1213-1245 Elsevier.
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