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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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 17006, Jan.
- Neil R. Ericsson, 2017, "How Biased Are U.S. Government Forecasts of the Federal Debt?," Working Papers, The George Washington University, The Center for Economic Research, number 2017-001, Jan.
- Manabu Asai & Michael McAleer, 2017, "Forecasting the volatility of Nikkei 225 futures," Documentos de Trabajo del ICAE, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico, number 2017-07, Jan.
- Bastianin, Andrea & Galeotti, Marzio & Manera, Matteo, 2016, "Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers," MPRA Paper, University Library of Munich, Germany, number 76308, Dec.
- Alessandro Barbarino & Efstathia Bura, 2017, "A Unified Framework for Dimension Reduction in Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2017-004, Jan, DOI: 10.17016/FEDS.2017.004.
- Dalibor Stevanovic & Rachidi Kotchoni & Maxime Leroux, 2017, "Forecasting economic activity in data-rich environment," CIRANO Working Papers, CIRANO, number 2017s-05, Jan.
- Alonso Fernández, Andrés Modesto & Bastos, Guadalupe & García-Martos, Carolina, 2017, "Electricity prices forecasting by averaging dynamic factor models," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de EstadÃstica, number 24028, Jan.
- 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, University of Barcelona, Regional Quantitative Analysis Group, number 201701, Jan, 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, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2017-03, Jan.
- Patrick Alexander & Jean-Philippe Cayen & Alex Proulx, 2017, "An Improved Equation for Predicting Canadian Non-Commodity Exports," Discussion Papers, Bank of Canada, number 17-1, DOI: 10.34989/sdp-2017-1.
- Zara Ghodsi & Allan Webster, 2017, "Forecasting UK Income Tax," BAFES Working Papers, Department of Accounting, Finance & Economic, Bournemouth University, number BAFES07, Jan.
- 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, University of St. Gallen, School of Finance, number 1622, Jul.
- Arnaud Dufays & Maciej Augustyniak & Luc Bauwens, 2016, "A new approach to volatility modeling: the High-Dimensional Markov model," Cahiers de recherche, Centre de recherche sur les risques, les enjeux économiques, et les politiques publiques, number 1609.