Report NEP-FOR-2022-06-27

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.

Other reports in NEP-FOR

The following items were announced in this report:

• Erlan Konebayev, 2022. "Forecasting a commodity-exporting small open developing economy using DSGE and DSGE-BVAR," NAC Analytica Working Paper 24, NAC Analytica, Nazarbayev University, revised May 2022.
• Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
• Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.
• Amélie Charles & Olivier Darné & Jae Kim, 2022. "Stock Return Predictability: Evaluation based on interval forecasts," Post-Print hal-03656310, HAL.
• Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.
• Vishal Kuber & Divakar Yadav & Arun Kr Yadav, 2022. "Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction," Papers 2205.06673, arXiv.org.