Report NEP-FOR-2019-08-26

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:

• Loermann, Julius & Maas, Benedikt, 2019. "Nowcasting US GDP with artificial neural networks," MPRA Paper 95459, University Library of Munich, Germany.
• Franses, Ph.H.B.F., 2019. "IMA(1,1) as a new benchmark for forecast evaluation," Econometric Institute Research Papers EI2019-28, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Franses, Ph.H.B.F., 2019. "Professional Forecasters and January," Econometric Institute Research Papers EI2019-25, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Daniel Borup & Erik Christian Montes Schütte, 2019. "In search of a job: Forecasting employment growth using Google Trends," CREATES Research Papers 2019-13, Department of Economics and Business Economics, Aarhus University.
• Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
• Jonathan Benchimol & Makram El-Shagi, 2019. "Forecast Performance in Times of Terrorism," Bank of Israel Working Papers 2019.08, Bank of Israel.
• Shen, Ze & Wan, Qing & Leatham, David J., 2019. "Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290696, Agricultural and Applied Economics Association.
• Fokin, Nikita & Polbin, Andrey, 2019. "A Bivariate Forecasting Model For Russian GDP Under Structural Changes In Monetary Policy and Long-Term Growth," MPRA Paper 95306, University Library of Munich, Germany, revised Apr 2019.
• Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
• Zahra Saki & Lori Rothenberg & Marguerite Moor & Ivan Kandilov & A. Blanton Godfrey, 2019. "Forecasting U.S. Textile Comparative Advantage Using Autoregressive Integrated Moving Average Models and Time Series Outlier Analysis," Papers 1908.04852, arXiv.org.