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Calculating Interval Forecasts

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  • Chatfield, Chris

Abstract

Several general approaches to calculating interval forecasts are described and compared. They include the use of theoretical formulae based on a fitted probability model, various "approximate" formulae (which should be avoided), and empirically-based, simulation, and resampling procedures. Some gener al comments are made as to why prediction intervals tend to be too narr ow in practice to encompass the required proportion of future observations. An example demonstrates the overriding importance of careful model specification.

Suggested Citation

  • Chatfield, Chris, 1993. "Calculating Interval Forecasts," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(2), pages 121-135, April.
  • Handle: RePEc:bes:jnlbes:v:11:y:1993:i:2:p:121-35
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    1. repec:bla:restud:v:57:y:1990:i:1:p:99-125 is not listed on IDEAS
    2. Gregory, Allan W, 1994. "Testing for Cointegration in Linear Quadratic Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 347-360, July.
    3. Hansen, Bruce E., 1992. "Efficient estimation and testing of cointegrating vectors in the presence of deterministic trends," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 87-121.
    4. Engle, R. F. & Granger, C. W. J. (ed.), 1991. "Long-Run Economic Relationships: Readings in Cointegration," OUP Catalogue, Oxford University Press, number 9780198283393.
    5. Engle, Robert F. & Yoo, Byung Sam, 1987. "Forecasting and testing in co-integrated systems," Journal of Econometrics, Elsevier, vol. 35(1), pages 143-159, May.
    6. Phillips, Peter C B & Ouliaris, S, 1990. "Asymptotic Properties of Residual Based Tests for Cointegration," Econometrica, Econometric Society, vol. 58(1), pages 165-193, January.
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