A state space approach to evaluate multi-horizon forecasts

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

No 2017-15, Working Papers from University of Tasmania, Tasmanian School of Business and Economics

Abstract: We propose a state space 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. We derive the conditions under which forecasts that contain error that is irrelevant to the target can still present the second moment bounds of rational forecasts. By evaluating multi-horizon daily maximum temperature forecasts for Melbourne, Australia, we demonstrate how this modeling framework analyzes 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 decision.

Keywords: Rational forecasts; implicit forecasts; forecast revision structure; weather forecasts (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Pages: 51 pages
Date: 2017
New Economics Papers: this item is included in nep-ets and nep-for
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Published by the University of Tasmania. Discussion paper 2017-15

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