Report NEP-FOR-2023-01-09

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:

• Rameshwar Garg & Shriya Barpanda & Girish Rao Salanke N S & Ramya S, 2022. "Machine Learning Algorithms for Time Series Analysis and Forecasting," Papers 2211.14387, arXiv.org.
• Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022. "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers 202258, University of Pretoria, Department of Economics.
• Thi Huyen Tran & Robert Ślepaczuk, 2022. "Quantile regression analysis to predict GDP distribution using data from the US and UK," Working Papers 2022-30, Faculty of Economic Sciences, University of Warsaw.
• Zhongchen Song & Tom Coupé, 2022. "Predicting Chinese consumption series with Baidu," Working Papers in Economics 22/19, University of Canterbury, Department of Economics and Finance.
• Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022. "Score-based calibration testing for multivariate forecast distributions," Papers 2211.16362, arXiv.org, revised Dec 2023.