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Extending Extended Logistic Regression for Ensemble Post-Processing: Extended vs. Separate vs. Ordered vs. Censored

Jakob W. Messner (), Georg J. Mayr (), Daniel S. Wilks () and Achim Zeileis ()

Working Papers from Faculty of Economics and Statistics, University of Innsbruck

Abstract: Extended logistic regression is a recent ensemble calibration method that extends logistic regression to provide full continuous probability distribution forecasts. It assumes conditional logistic distributions for the (transformed) predictand and fits these using selected predictand category probabilities. In this study we compare extended logistic regression to the closely related ordered and censored logistic regression models. Ordered logistic regression avoids the logistic distribution assumption but does not yield full probability distribution forecasts, whereas censored regression directly fits the full conditional predictive distributions. To compare the performance of these and other ensemble post-processing methods we used wind speed and precipitation data from two European locations and ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF). Ordered logistic regression performed similarly to extended logistic regression for probability forecasts of discrete categories whereas full predictive distributions were better predicted by censored regression.

Keywords: probabilistic forecasting; extended logistic regression; ordered logistic regression; heteroscedasticity (search for similar items in EconPapers)
JEL-codes: C53 C25 Q42 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2013-10
New Economics Papers: this item is included in nep-dcm and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:inn:wpaper:2013-32

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