Report NEP-FOR-2020-01-27

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:

• Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2020. "Capturing Macroeconomic Tail Risks with Bayesian Vector Autoregressions," Working Papers 20-02R, Federal Reserve Bank of Cleveland, revised 22 Sep 2020.
• Gergely Ganics & Barbara Rossi & Tatevik Sekhposyan, 2019. "From fixed-event to fixed-horizon density forecasts: Obtaining measures of multi-horizon uncertainty from survey density forecasts," Economics Working Papers 1689, Department of Economics and Business, Universitat Pompeu Fabra.
• Claudio, João C. & Heinisch, Katja & Holtemöller, Oliver, 2019. "Nowcasting East German GDP growth: A MIDAS approach," IWH Discussion Papers 24/2019, Halle Institute for Economic Research (IWH).
• Alexander Glas & Matthias Hartmann, 2020. "Uncertainty measures from partially rounded probabilistic forecast surveys," Working Papers 427, University of Milano-Bicocca, Department of Economics, revised Jan 2020.
• Li, W. & Fok, D. & Franses, Ph.H.B.F., 2019. "Forecasting own brand sales: Does incorporating competition help?," Econometric Institute Research Papers EI2019-35, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
• Brüggen, Alexander & Grabner, Isabella & Sedatole, Karen, 2020. "The folly of forecasting: The effects of a disaggregated demand forecasting system on forecast error, forecast positive bias, and inventory levels," Department for Strategy and Innovation Working Paper Series 07/2020, WU Vienna University of Economics and Business.
• Pedro Miguel Avelino Bação & António Portugal Duarte & Diogo Viveiros, 2020. "Exports Since the International Financial Crisis," CeBER Working Papers 2020-01, Centre for Business and Economics Research (CeBER), University of Coimbra.
• Osipov, Vasiliy & Zhukova, Nataly & Miloserdov, Dmitriy, 2019. "Neural Network Associative Forecasting of Demand for Goods," MPRA Paper 97314, University Library of Munich, Germany, revised 23 Sep 2019.
• Timothy Neal & Michael Keane, 2020. "Comparing Deep Neural Network and Econometric Approaches to Predicting the Impact of Climate Change on Agricultural Yield," Discussion Papers 2020-02, School of Economics, The University of New South Wales.