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Report NEP-FOR-2024-11-11
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:
- Yang Liu & Ran Pan & Rui Xu, 2024, "Mending the Crystal Ball: Enhanced Inflation Forecasts with Machine Learning," IMF Working Papers, International Monetary Fund, number 2024/206, Sep.
- Zongwu Cai & Gunawan & Yuying Sun, 2024, "A New Nonparametric Combination Forecasting with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202412, Sep, revised Sep 2024.
- Ali Mehrabani & Shahnaz Parsaeian & Aman Ullah, 2024, "Shrinkage Estimation and Forecasting in Dynamic Regression Models under Structural Instability," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202410, Aug.
- Theophilus G. Baidoo & Ashley Obeng, 2024, "Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies," Papers, arXiv.org, number 2410.05630, Oct.
- Liu, Yirui & Qiao, Xinghao & Pei, Yulong & Wang, Liying, 2024, "Deep functional factor models: forecasting high-dimensional functional time series via Bayesian nonparametric factorization," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 125587, Jul.
- Allen, Sam & Koh, Jonathan & Segers, Johan & Ziegel, Johanna, 2024, "Tail calibration of probabilistic forecasts," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2024018, Jul.