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An unobserved component modeling approach to evaluate multi-horizon forecasts (Discussion Paper 2018-04)

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Tian, J and Goodwin, T 2018 , An unobserved component modeling approach to evaluate multi-horizon forecasts (Discussion Paper 2018-04).

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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.

Item Type: Report (Discussion Paper)
Authors/Creators:Tian, J and Goodwin, T
Keywords: Decision making, decomposition, evaluating forecasts, state space models, weather forecasting
Publisher: University of Tasmania
Additional Information:

JEL classification: C32; C53

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