Report NEP-FOR-2022-05-02

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:

• Md Rashidul Hasan & Muntasir A Kabir & Rezoan A Shuvro & Pankaz Das, 2022. "A Comparative Study on Forecasting of Retail Sales," Papers 2203.06848, arXiv.org.
• Jun Lu & Shao Yi, 2022. "Reducing overestimating and underestimating volatility via the augmented blending-ARCH model," Papers 2203.12456, arXiv.org.
• Tobias Fissler & Hajo Holzmann, 2022. "Measurability of functionals and of ideal point forecasts," Papers 2203.08635, arXiv.org.
• Cameron Fen & Samir Undavia, 2022. "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers 2203.06540, arXiv.org.
• Naimoli, Antonio, 2022. "The information content of sentiment indices for forecasting Value at Risk and Expected Shortfall in equity markets," MPRA Paper 112588, University Library of Munich, Germany.
• Wheatcroft, Edward, 2020. "A profitable model for predicting the over/under market in football," LSE Research Online Documents on Economics 103712, London School of Economics and Political Science, LSE Library.
• Ishu Gupta & Tarun Kumar Madan & Sukhman Singh & Ashutosh Kumar Singh, 2022. "HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model," Papers 2203.08143, arXiv.org.
• Darren Shannon & Grigorios Fountas, 2022. "Amending the Heston Stochastic Volatility Model to Forecast Local Motor Vehicle Crash Rates: A Case Study of Washington, D.C," Papers 2203.01729, arXiv.org.
• Javad T. Firouzjaee & Pouriya Khaliliyan, 2022. "Machine learning model to project the impact of Ukraine crisis," Papers 2203.01738, arXiv.org.