An unobserved component modeling approach to evaluate multi-horizon forecasts

Jing Tian () and Thomas Goodwin
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Jing Tian: Tasmanian School of Business & Economics, University of Tasmania, http://www.utas.edu.au/profiles/staff/economics/jing-tian
Thomas Goodwin: http://www.tasfoods.com.au/

No 2018-04, Working Papers from University of Tasmania, Tasmanian School of Business and Economics

Abstract: We propose an unobserved modeling framework to evaluate a set of forecasts that target the same variable but are updated along the forecast horizon. The approach decomposes forecast errors into three distinct horizon-specific processes, namely, bias, rational error and implicit error, and attributes forecast revisions to corrections for these forecast errors. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework can be used to analyze the dynamics of the forecast revision structure across horizons. Understanding forecast revisions is critical for weather forecast users to determine the optimal timing for their planning decisions.

Keywords: Decision making; decomposition; evaluating forecasts; state space models; weather forecasting (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2018
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-for
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Published by the University of Tasmania. Discussion paper 2018-04

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