Assessing the Point Predictions

  • Michael P. Clements
Part of the Palgrave Texts in Econometrics book series (PTEC)


Point forecasts can typically be evaluated in terms of their accuracy relative to a rival set of forecasts, or in terms of whether they possess certain desirable characteristics, such as the forecast and forecast error being uncorrelated. There are differences between the evaluation of survey and model forecasts. An obvious one is that survey forecasts cannot be compared to their expected accuracy given the in-sample fit (past track record) of the model, since their provenance is unknown. Comparisons of forecasts from rival models typically seek to compare the models (in population). The evaluation of rival sets of survey forecasts rests squarely on the forecasts themselves. Tests of equal accuracy or forecast encompassing, as well as tests of whether the forecasts possess desirable characteristics, such as forecast efficiency, can be applied globally over the whole sample of forecasts, or locally on sub-samples, allowing for instabilities in performance over time. Also considered is the appropriate way of testing for forecast rationality or efficiency in a panel context, where the options include testing the aggregate, pooling the observations over individuals and time periods, or running separate regressions for each individual. A key assumption is that individuals weigh over- and under-prediction equally that the loss function is symmetric. This assumption is testable.


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© The Author(s) 2019

Authors and Affiliations

  • Michael P. Clements
    • 1
  1. 1.ICMA Centre, Henley Business SchoolUniversity of ReadingWheatleyUK

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