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Report NEP-FOR-2020-10-26
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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-FOR
The following items were announced in this report:
- Alessandro Giovannelli & Daniele Massacci & Stefano Soccorsi, 2020, "Forecasting Stock Returns with Large Dimensional Factor Models," Working Papers, Lancaster University Management School, Economics Department, number 305661169.
- Olivier Darné & Amelie Charles, 2020, "Nowcasting GDP growth using data reduction methods: Evidence for the French economy," Post-Print, HAL, number hal-02948802, Sep.
- Gael M. Martin & Rub'en Loaiza-Maya & David T. Frazier & Worapree Maneesoonthorn & Andr'es Ram'irez Hassan, 2020, "Optimal probabilistic forecasts: When do they work?," Papers, arXiv.org, number 2009.09592, Sep.
- Dimitrakopoulos, Stefanos & Tsionas, Mike G. & Aknouche, Abdelhakim, 2020, "Ordinal-response models for irregularly spaced transactions: A forecasting exercise," MPRA Paper, University Library of Munich, Germany, number 103250, Oct, revised 01 Oct 2020.
- Zongwu Cai & Chaoqun Ma & Xianhua Mi, 2020, "Realized Volatility Forecasting Based on Dynamic Quantile Model Averaging," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202016, Sep, revised Sep 2020.
- Timo Dimitriadis & Xiaochun Liu & Julie Schnaitmann, 2020, "Encompassing Tests for Value at Risk and Expected Shortfall Multi-Step Forecasts based on Inference on the Boundary," Papers, arXiv.org, number 2009.07341, Sep.
- Ruths Sion, Sebastian, 2020, "Time Series Analyses of Global Oil Prices: Shocks, Effects and Predictability," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 123161.
- Rashad Ahmed & M. Hashem Pesaran, 2020, "Regional Heterogeneity and U.S. Presidential Elections," CESifo Working Paper Series, CESifo, number 8615.
- Hamed Vaheb, 2020, "Asset Price Forecasting using Recurrent Neural Networks," Papers, arXiv.org, number 2010.06417, Oct, revised Oct 2020.
- Sidra Mehtab & Jaydip Sen & Abhishek Dutta, 2020, "Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models," Papers, arXiv.org, number 2009.10819, Sep.
- Jonathan Berrisch & Florian Ziel, 2020, "Distributional Modeling and Forecasting of Natural Gas Prices," Papers, arXiv.org, number 2010.06227, Oct, revised Aug 2021.
- Xin Sheng & Rangan Gupta & Qiang Ji, 2020, "Forecasting Charge-Off Rates with a Panel Tobit Model: The Role of Uncertainty," Working Papers, University of Pretoria, Department of Economics, number 202092, Oct.
- Kladivko, Kamil & Österholm, Pär, 2020, "Can Households Predict where the Macroeconomy is Headed?," Working Papers, Örebro University, School of Business, number 2020:11, Oct.
- Karimova, Amira, 2020, "Forecast of Ontario’s housing stock 2020-2046," MPRA Paper, University Library of Munich, Germany, number 103298, Aug.