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Report NEP-FOR-2020-11-23
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:
- Christiane Baumeister & Pierre Guérin, 2020, "A Comparison of Monthly Global Indicators for Forecasting Growth," CESifo Working Paper Series, CESifo, number 8656.
- Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020, "Prediction for the 2020 United States Presidential Election using Machine Learning Algorithm: Lasso Regression," MPRA Paper, University Library of Munich, Germany, number 103889, Oct, revised 31 Oct 2020.
- Dimitriadis, Timo & Liu, Xiaochun & Schnaitmann, Julie, 2020, "Encompassing tests for value at risk and expected shortfall multi-step forecasts based on inference on the boundary," Hohenheim Discussion Papers in Business, Economics and Social Sciences, University of Hohenheim, Faculty of Business, Economics and Social Sciences, number 11-2020.
- Sinha, Pankaj & Verma, Aniket & Shah, Purav & Singh, Jahnavi & Panwar, Utkarsh, 2020, "Prediction for the 2020 United States Presidential Election using Linear Regression Model," MPRA Paper, University Library of Munich, Germany, number 103890, Sep, revised 20 Oct 2020.
- Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020, "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," MPRA Paper, University Library of Munich, Germany, number 103779, Oct.
- Timothy Christensen & Hyungsik Roger Moon & Frank Schorfheide, 2020, "Robust Forecasting," Papers, arXiv.org, number 2011.03153, Nov, revised Dec 2020.
- Tae-Hwy Lee & Ekaterina Seregina, 2020, "Learning from Forecast Errors: A New Approach to Forecast Combinations," Papers, arXiv.org, number 2011.02077, Nov, revised May 2021.
- Steven Lehrer & Tian Xie, 2020, "The Bigger Picture: Combining Econometrics with Analytics Improve Forecasts of Movie Success," Working Paper, Economics Department, Queen's University, number 1449, Oct.
- Zhang, Bo & Nguyen, Bao H., 2020, "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-12.
- Dimitriadis, Timo & Patton, Andrew J. & Schmidt, Patrick W., 2020, "Testing forecast rationality for measures of central tendency," Hohenheim Discussion Papers in Business, Economics and Social Sciences, University of Hohenheim, Faculty of Business, Economics and Social Sciences, number 12-2020.
- Soh, Ann-Ni, 2020, "A Review on the Leading Indicator Approach towards Economic Forecasting," MPRA Paper, University Library of Munich, Germany, number 103854, Oct.
- Constandina Koki & Stefanos Leonardos & Georgios Piliouras, 2020, "Exploring the Predictability of Cryptocurrencies via Bayesian Hidden Markov Models," Papers, arXiv.org, number 2011.03741, Nov, revised Dec 2020.
- Guglielmo Maria Caporale & Luis A. Gil-Alana, 2020, "Modelling Loans to Non-Financial Corporations within the Eurozone: A Long-Memory Approach," CESifo Working Paper Series, CESifo, number 8674.
- Niko Hauzenberger & Michael Pfarrhofer & Luca Rossini, 2020, "Sparse time-varying parameter VECMs with an application to modeling electricity prices," Papers, arXiv.org, number 2011.04577, Nov, revised Apr 2023.