Report NEP-FOR-2017-01-29

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:

• Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
• Neil R. Ericsson, 2017. "How Biased Are U.S. Government Forecasts of the Federal Debt?," Working Papers 2017-001, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
• Manabu Asai & Michael McAleer, 2017. "Forecasting the volatility of Nikkei 225 futures," Documentos de Trabajo del ICAE 2017-07, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
• Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2016. "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," MPRA Paper 76308, University Library of Munich, Germany.
• Alessandro Barbarino & Efstathia Bura, 2017. "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series 2017-004, Board of Governors of the Federal Reserve System (U.S.).
• Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017. "Forecasting economic activity in data-rich environment," CIRANO Working Papers 2017s-05, CIRANO.
• Bastos, Guadalupe & García-Martos, Carolina, 2017. "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS 24028, Universidad Carlos III de Madrid. Departamento de Estadística.
• Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting”," AQR Working Papers 201701, University of Barcelona, Regional Quantitative Analysis Group, revised Jan 2017.
• Gunes Kamber & James Morley & Benjamin Wong, 2017. "Intuitive and reliable estimates of the output gap from a Beveridge-Nelson Filter," CAMA Working Papers 2017-03, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
• Patrick Alexander & Jean-Philippe Cayen & Alex Proulx, 2017. "An Improved Equation for Predicting Canadian Non-Commodity Exports," Discussion Papers 17-1, Bank of Canada.
• Zara Ghodsi & Allan Webster, 2017. "Forecasting UK Income Tax," BAFES Working Papers BAFES07, Department of Accounting, Finance & Economic, Bournemouth University.
• Hagfors, Lars Ivar & Kamperud , Hilde Horthe & Paraschiv, Florentina & Prokopczuk, Marcel & Sator, Alma & Westgaard, Sjur, 2016. "Prediction of Extreme Price Occurrences in the German Day-ahead Electricity Market," Working Papers on Finance 1622, University of St. Gallen, School of Finance.
• Arnaud Dufays & Maciej Augustyniak & Luc Bauwens, 2016. "A new approach to volatility modeling: the High-Dimensional Markov model," Cahiers de recherche 1609, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques.