An examination of the accuracy of judgemental confidence intervals in time series forecasting

Marcus J. O’Connor · Michael J. Lawrence

48 citations0/0 files storedJournal of Forecasting

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Forecasting Techniques and Applications Probability and Statistical Research Decision-Making and Behavioral Economics

Abstract

Abstract This paper examines the accuracy (calibration) of judgemental and selected statistical confidence intervals in time series forecasting. Using the forecasts and forecast errors produced by the deseasonalized single exponential smoothing method, three statistical intervals were produced utilizing three assumptions about the distribution of errors: the normal distribution, the empirical distribution and the distribution based on the Chebyshev inequality. Using 33 real‐life time series, results indicated that the judgemental confidence intervals were initially excessively overconfident, thus confirming the finding of Lichtenstein et al. (1982). However, calibration of the judgemental intervals improved considerably with feedback, and was found to be influenced by the degree of forecasting difficulty of the series.

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References

1974Judgment under Uncertainty: Heuristics and Biases 1975Judgment under Uncertainty: Heuristics and Biases 1982The accuracy of extrapolation (time series) methods: Results of a forecasting competition 1975Cognitive Processes and the Assessment of Subjective Probability Distributions 1975Knowledge and Cognition 1983State of the Art—Encoding Subjective Probabilities: A Psychological and Psychometric Review 1972Probabilistic forecasting: An experiment related to the stock market 1977Decision Making and Change in Human Affairs

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