Abstract
Computing prediction intervals (PIs) is an important part of the forecasting process intended to indicate the likely uncertainty in point forecasts. The commonest method of calculating PIs is to use theoretical formulae conditional on a best-fitting model. If a normality assumption is used, it needs to be checked. Alternative computational procedures that are not so dependent on a fitted model include the use of empirically based and re-sampling methods. Some so-called approximate formulae should be avoided. PIs tend to be too narrow because out-of-sample forecast accuracy is often poorer than would be expected from within-sample fit, particularly for PIs calculated conditional on a model fitted to past data. Reasons for this include uncertainty about the model and a changing environment. Ways of overcoming these problems include using a mixture of models with a Bayesian approach and using a forecasting method that is designed to be robust to changes in the underlying model.
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References
Abraham, B. J. Ledolter (1983), Statistical Methods for Forecasting. New York: Wiley. Arkes, H. (2001), “Overconfidence in judgmental forecasting,” in J. S. Armstrong, Principles of Forecasting. Norwell, MA: Kluwer Academic Publishers.
Armstrong, J. S. (1985), Long-Range Forecasting, 2nd ed. New York: Wiley. Full text at hops.wharton.upenn.edu/forecast.
Armstrong, J. S. F. Collopy (1992), “Error measures for generalizing about forecasting methods: Empirical comparisons,” International Journal of Forecasting, 8, 69–80. Full text at hops.wharton.upenn.edu/forecast
Armstrong, J. S. F. Collopy (2001), “Identification of asymmetric prediction intervals through causal forces” Journal of Forecasting (forthcoming).
Ashley, R. (1988), “On the relative worth of recent macroeconomic forecasts,” International Journal of Forecasting, 4, 363–376.
Barnett, G., R. Kohn S. J. Sheather (1996), “Robust estimation of an autoregressive model using Markov chain Monte Carlo,” Journal of Econometrics, 74, 237–254.
Barnett, G., R. Kohn S. J. Sheather (1997), “Robust Bayesian estimation of autoregresive-moving average models,” Journal of Time Series Analysis, 18, 11–28.
Bowerman, B. L. R. T. O’Connell (1987), Time Series Forecasting, 2nd ed. Boston: Duxbury Press.
Box, G. E. P., G. M. Jenkins G.C. Reinsel (1994), Time-Series Analysis, Forecasting and Control, (3rd ed.) San Francisco: Holden-Day.
Brockwell, P. J. R.A. Davis (1991), Time Series: Theory and Methods, (2nd ed.) New York: Springer-Verlag.
Chatfield, C. (1993), “Calculating interval forecasts” (with discussion), Journal of Business and Economic Statistics, 11, 121–144.
Chatfield, C. (1996a), The Analysis of Time Series, (5th ed.) London: Chapman and Hall. Chatfield, C. (1996b), “Model uncertainty and forecast accuracy,” Journal of Forecasting, 15, 495–508.
Chatfield, C. M. Yar (1991), “Prediction intervals for multiplicative Holt-Winters,” International Journal of Forecasting, 7, 31–37.
Christoffersen, P. F. (1998) “Evaluating interval forecasts,” International Economic Review, 39, 841–862.
Clemen, R. T. (1989), “Combining forecasts: A review and annotated bibliography,” International Journal of Forecasting, 5, 559–583.
Dalrymple, D. J. (1987), “Sales forecasting practices: Results from a United States survey,” International Journal of Forecasting, 3, 379–391.
Draper, D. (1995), “Assessment and propagation of model uncertainty” (with discussion), Journal of the Royal Statistical Society, Series B, 57, 45–97.
Faraway, J. C. Chatfield (1998), “Time-series forecasting with neural networks: A comparative study using the airline data,” Applied Statistics, 47, 231–250.
Gardner, E. S. Jr. (1988), “A simple method of computing prediction intervals for time series forecasts,” Management Science, 34, 541–546.
Granger, C. W. J. (1996), “Can we improve the perceived quality of economic forecasts?” Journal of Applied Econometrics, 11, 455–473.
Granger, C. W. J. P. Newbold (1986), Forecasting Economic Time Series, (2nd ed.) New York: Academic Press.
Harrison, P. J. (1967), “Exponential smoothing and short-term sales forecasting,” Management Science, 13, 821–842.
Harvey, A. C. (1989), Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge: Cambridge University Press.
Hyndman, R. J. (1995), “Highest-density forecast regions for non-linear and non-normal time series models,” Journal of Forecasting, 14, 431–441.
Ledolter, J. (1989), “The effect of additive outliers on the forecasts from ARIMA models,” International Journal of Forecasting, 5, 231–240.
Lutkepohl, H. (1991), Introduction to Multiple Time Series Analysis. Berlin: Springer-Verlag.
Makridakis, S. (1988), “Metaforecasting,” International Journal of Forecasting, 4, 467–491
Makridakis, S., M. Hibon, E. Lusk M. Belhadjali (1987), “Confidence intervals: An empirical investigation of the series in the M-Competition,” International Journal of Forecasting, 3, 489–508.
Makridakis, S. R. L. Winkler (1989), “Sampling distributions of post-sample forecasting errors,” Applied Statistics, 38, 331–342.
McCullough, B. D. (1994), “Bootstrapping forecast intervals: An application to AR(p) models,” Journal of Forecasting, 13, 51–66.
McCullough, B. D. (1996), “Consistent forecast intervals when the forecast-period exogenous variables are stochastic,” Journal of Forecasting, 15, 293–304.
Meade, N. T. Islam (1995), “Prediction intervals for growth curve forecasts,” Journal of Forecasting, 14, 413–430.
O’Connor, M. J. M. J. Lawrence (1989), “An examination of the accuracy of judgmental confidence intervals in time series forecasting,” Journal of Forecasting, 8, 141–155.
O’Connor, M. J. M. J. Lawrence (1992), “Time series characteristics and the widths of judgmental confidence intervals,” International Journal of Forecasting, 7, 413–1120.
Picard, R. R. R. D. Cook (1984), “Cross-validation of regression models,” Journal of the American Statistical Association, 79, 575–583.
Ravishankar, N., L. S-Y. Wu J. Glaz (1991), “Multiple prediction intervals for time series: Comparison of simultaneous and marginal intervals,” Journal of Forecasting, 10, 445–463.
Tay, A. S. K. F. Wallis (2000), “Density forecasting: A survey,” Journal of Forecasting, 19, 235–254.
Thombs, L. A. W. R. Schucany (1990), “Bootstrap prediction intervals for autoregression,” Journal of the American Statistical Association, 85, 486–492.
Thompson, P. A. R. B. Miller (1986), “Sampling the future: A Bayesian approach to forecasting from univariate time series models,” Journal of Business Economic Statistics, 4, 427–436.
Tong, H. (1990), Non-Linear Time Series. Oxford: Clarendon Press.
Veall, M. R. (1989), “Applications of computationally-intensive methods to econometrics,” Bulletin of the I.S.I., 47th Session, 75–88.
Wei, W. W. S. (1990), Time Series Analysis. Redwood City, CA: Addison-Wesley.
West, M. J. Harrison (1997), Bayesian Forecasting and Dynamic Models,(2nd ed.) New York: Springer-Verlag.
Williams, W. H. M. L. Goodman (1971), “A simple method for the construction of empirical confidence limits for economic forecasts,” Journal of the American Statistical Association, 66, 752–754.
Wright, G., M. J. Lawrence F. Collopy (1996), “Editorial: The role and validity of judgement in forecasting,” International Journal of Forecasting, 12, 1–8
Yar, M. C. Chatfield (1990), “Prediction intervals for the Holt-Winters forecasting procedure,” International Journal of Forecasting, 6, 127–137.
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Chatfield, C. (2001). Prediction Intervals for Time-Series Forecasting. In: Armstrong, J.S. (eds) Principles of Forecasting. International Series in Operations Research & Management Science, vol 30. Springer, Boston, MA. https://doi.org/10.1007/978-0-306-47630-3_21
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DOI: https://doi.org/10.1007/978-0-306-47630-3_21
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