Report NEP-FOR-2018-07-23

This is the archive for NEP-FOR, a report on new working papers in the area of Forecasting. Rob J Hyndman issued this report. It is usually issued weekly.

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Other reports in NEP-FOR

The following items were announced in this report:

• Sebastian J. Dietz & Philipp Kneringer & Georg J. Mayr & Achim Zeileis, 2018. "Low visibility forecasts for different flight planning horizons using tree-based boosting models," Working Papers 2018-11, Faculty of Economics and Statistics, Universität Innsbruck.
• Thorsten Simon & Georg J. Mayr & Nikolaus Umlauf & Achim Zeileis, 2018. "Lightning Prediction Using Model Output Statistics," Working Papers 2018-14, Faculty of Economics and Statistics, Universität Innsbruck.
• Florian Huber & Gregor Kastner & Michael Pfarrhofer, 2018. "Introducing shrinkage in heavy-tailed state space models to predict equity excess returns," Papers 1805.12217, arXiv.org, revised Jul 2019.
• Alexey Ivashchenko, 2017. "Credit Spreads, Daily Business Cycle, and Corporate Bond Returns Predictability," Swiss Finance Institute Research Paper Series 17-67, Swiss Finance Institute, revised Jan 2018.
• Dominique Guégan & Matteo Iacopini, 2018. "Nonparameteric forecasting of multivariate probability density functions," Documents de travail du Centre d'Economie de la Sorbonne 18012, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
• Gálvez-Soriano Oscar de Jesús, 2018. "Nowcasting Mexican GDP using Factor Models and Bridge Equations," Working Papers 2018-06, Banco de México.
• Kotilainen, Markku, 2018. "Forecasting Activity at Etla in 1971–2018," ETLA Brief 69, The Research Institute of the Finnish Economy.
• Yu-Long Zhou & Ren-Jie Han & Qian Xu & Wei-Ke Zhang, 2018. "Long Short-Term Memory Networks for CSI300 Volatility Prediction with Baidu Search Volume," Papers 1805.11954, arXiv.org.
• Dominique Guegan & Matteo Iacopini, 2018. "Nonparametric forecasting of multivariate probability density functions," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01821815, HAL.