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Report NEP-FOR-2022-04-04
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:
- Chao Zhang & Yihuang Zhang & Mihai Cucuringu & Zhongmin Qian, 2022, "Volatility forecasting with machine learning and intraday commonality," Papers, arXiv.org, number 2202.08962, Feb, revised Feb 2023.
- Todd E. Clark & Florian Huber & Gary Koop & Massimiliano Marcellino, 2022, "Forecasting US Inflation Using Bayesian Nonparametric Models," Working Papers, Federal Reserve Bank of Cleveland, number 22-05, Mar, DOI: 10.26509/frbc-wp-202205.
- Morita, Hiroshi, 2022, "Forecasting GDP growth using stock returns in Japan: A factor-augmented MIDAS approach," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-118, Mar.
- Hagher Ben Rhomdhane & Brahim Mehdi Benlallouna, 2022, "Nowcasting real GDP in Tunisia using large datasets and mixed-frequency models," IHEID Working Papers, Economics Section, The Graduate Institute of International Studies, number 02-2022, Mar.
- Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022, "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 674, Mar.
- Tae-Hwy Lee & Shahnaz Parsaeian & Aman Ullah, 2022, "Optimal Forecast under Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202207, Jan.
- David T. Frazier & Gael M. Martin & Ruben Loaiza-Maya, 2022, "Variational Bayes in State Space Models: Inferential and Predictive Accuracy," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 1/22.
- Jurić, Tado, 2022, "Predicting refugee flows from Ukraine with an approach to Big (Crisis) Data: a new opportunity for refugee and humanitarian studies," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 251215, DOI: 10.1101/2022.03.15.22272428.