Printed from https://ideas.repec.org/n/nep-for/2023-01-09.html
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.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, arXiv.org, number 2211.14387, Nov.
- Jiawen Luo & Oguzhan Cepni & Riza Demirer & Rangan Gupta, 2022, "Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies," Working Papers, University of Pretoria, Department of Economics, number 202258, Dec.
- Thi Huyen Tran & Robert Ślepaczuk, 2022, "Quantile regression analysis to predict GDP distribution using data from the US and UK," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-30.
- Zhongchen Song & Tom Coupé, 2022, "Predicting Chinese consumption series with Baidu," Working Papers in Economics, University of Canterbury, Department of Economics and Finance, number 22/19, Dec.
- Malte Knuppel & Fabian Kruger & Marc-Oliver Pohle, 2022, "Score-based calibration testing for multivariate forecast distributions," Papers, arXiv.org, number 2211.16362, Nov, revised Dec 2023.