Empirical prediction intervals revisited

Yun Shin Lee and Stefan Scholtes

International Journal of Forecasting, 2014, vol. 30, issue 2, 217-234

Abstract: Empirical prediction intervals are constructed based on the distribution of previous out-of-sample forecast errors. Given historical data, a sample of such forecast errors is generated by successively applying a chosen point forecasting model to a sequence of fixed windows of past observations and recording the associated deviations of the model predictions from the actual observations out-of-sample. The suitable quantiles of the distribution of these forecast errors are then used along with the point forecast made by the selected model to construct an empirical prediction interval. This paper re-examines the properties of the empirical prediction interval. Specifically, we provide conditions for its asymptotic validity, evaluate its small sample performance and discuss its limitations.

Keywords: Interval forecasting; Probabilistic forecasting; Out-of-sample forecast error; Model uncertainty; Non-Gaussian distribution (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:30:y:2014:i:2:p:217-234

DOI: 10.1016/j.ijforecast.2013.07.018

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