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  1. That is, a percentage is valid on a ratio scale, but not on an interval scale. Only ratio scale variables have meaningful zeros.↩︎

  2. Lawrence, Goodwin, O’Connor, & Önkal (2006)↩︎

  3. Fildes & Goodwin (2007b)↩︎

  4. These are Australian dollar amounts published by the Australian government for 2012.↩︎

  5. For further reading, refer to: Rowe (2007); Rowe & Wright (1999)↩︎

  6. Buehler, Messervey, & Griffin (2005)↩︎

  7. This example is extracted from Kahneman & Lovallo (1993)↩︎

  8. Groves et al. (2009)↩︎

  9. Randall & Wolff (1994)↩︎

  10. GA was an observer on this technical committee for a few years.↩︎

  11. Athanasopoulos & Hyndman (2008)↩︎

  12. In some books it is called “single exponential smoothing”.↩︎

  13. More precisely, if \(\{y_t\}\) is a stationary time series, then for all \(s\), the distribution of \((y_t,\dots,y_{t+s})\) does not depend on \(t\).↩︎

  14. arc cos is the inverse cosine function. You should be able to find it on your calculator. It may be labelled acos or cos\(^{-1}\).↩︎

  15. A convenient way to produce a time plot, ACF plot and PACF plot in one command is to use the gg_tsdisplay() function.↩︎

  16. As already noted, comparing information criteria is only valid for ARIMA models of the same orders of differencing.↩︎

  17. Forecasting with cointegrated models is discussed by Harris & Sollis (2003).↩︎

Bibliography

Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29(1), 19–31. https://robjhyndman.com/publications/aus-domestic-tourism/

Buehler, R., Messervey, D., & Griffin, D. (2005). Collaborative planning and prediction: Does group discussion affect optimistic biases in time estimation? Organizational Behavior and Human Decision Processes, 97(1), 47–63. https://doi.org/10.1016/j.obhdp.2005.02.004

Fildes, R., & Goodwin, P. (2007b). Good and bad judgment in forecasting: Lessons from four companies. Foresight: The International Journal of Applied Forecasting, (8), 5–10. https://fpc.forecasters.org/2007/10/01/31963/

Groves, R. M., Fowler, F. J., Couper, M. P., Lepkowski, J. M., Singer, E., & Tourangeau, R. (2009). Survey methodology (2nd ed). John Wiley & Sons. [Amazon]

Harris, R., & Sollis, R. (2003). Applied time series modelling and forecasting. Chichester, UK: John Wiley & Sons. [Amazon]

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive perspective on risk taking. Management Science, 39(1), 17–31. https://doi.org/10.1287/mnsc.39.1.17

Lawrence, M., Goodwin, P., O’Connor, M., & Önkal, D. (2006). Judgmental forecasting: A review of progress over the last 25 years. International Journal of Forecasting, 22(3), 493–518. https://doi.org/10.1016/j.ijforecast.2006.03.007

Randall, D. M., & Wolff, J. A. (1994). The time interval in the intention-behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33, 405–418. https://doi.org/10.1111/j.2044-8309.1994.tb01037.x

Rowe, G. (2007). A guide to Delphi. Foresight: The International Journal of Applied Forecasting, (8), 11–16. https://fpc.forecasters.org/2007/10/01/31964/

Rowe, G., & Wright, G. (1999). The Delphi technique as a forecasting tool: Issues and analysis. International Journal of Forecasting, 15, 353–375. https://doi.org/10.1016/S0169-2070(99)00018-7