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Report NEP-FOR-2022-01-10
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:
- Easaw, Joshy & Fang, Yongmei & Heravi, Saeed, 2021, "Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model," Cardiff Economics Working Papers, Cardiff University, Cardiff Business School, Economics Section, number E2021/34, Dec.
- Congressional Budget Office, 2021, "CBO’s Economic Forecasting Record: 2021 Update," Reports, Congressional Budget Office, number 57579, Dec.
- Ashish Kumar & Abeer Alsadoon & P. W. C. Prasad & Salma Abdullah & Tarik A. Rashid & Duong Thu Hang Pham & Tran Quoc Vinh Nguyen, 2021, "Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction," Papers, arXiv.org, number 2112.03946, Nov.
- Nyoni, Thabani, 2021, "Modeling and forecasting international tourism demand in Zimbabwe: a bright future for Zimbabwe's tourism industry," MPRA Paper, University Library of Munich, Germany, number 110901, Dec, revised 01 Dec 2021.
- Kitova, Olga & Savinova, Victoria, 2021, "Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods," MPRA Paper, University Library of Munich, Germany, number 110824, Nov.
- Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022, "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers, University of Pretoria, Department of Economics, number 202201, Jan.
- Delis, Panagiotis & Degiannakis, Stavros & Giannopoulos, Kostantinos, 2021, "What should be taken into consideration when forecasting oil implied volatility index?," MPRA Paper, University Library of Munich, Germany, number 110831, Nov.
- Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021, "Expert Aggregation for Financial Forecasting," Papers, arXiv.org, number 2111.15365, Nov, revised Jul 2023.
- Kajal Lahiri & Junyan Zhang & Yongchen Zhao, 2021, "Inefficiency in Social Security Trust Funds Forecasts," CESifo Working Paper Series, CESifo, number 9415.
- Shujian Liao & Jian Chen & Hao Ni, 2021, "Forex Trading Volatility Prediction using Neural Network Models," Papers, arXiv.org, number 2112.01166, Dec, revised Dec 2021.
- Chris Redl & Sandile Hlatshwayo, 2021, "Forecasting Social Unrest: A Machine Learning Approach," IMF Working Papers, International Monetary Fund, number 2021/263, Nov.