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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, or Bluesky.
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, NAC Analytica, Nazarbayev University, number 24, Apr, revised May 2022.
- Micha{l} Narajewski, 2022, "Probabilistic forecasting of German electricity imbalance prices," Papers, arXiv.org, number 2205.11439, May.
- Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022, "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers, arXiv.org, number 2205.07719, May.
- Amélie Charles & Olivier Darné & Jae Kim, 2022, "Stock Return Predictability: Evaluation based on interval forecasts," Post-Print, HAL, number hal-03656310, Apr, DOI: 10.1111/boer.12298.
- 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, National Institute of Economic and Social Research, number 538, May.
- Vishal Kuber & Divakar Yadav & Arun Kr Yadav, 2022, "Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction," Papers, arXiv.org, number 2205.06673, May.