Printed from https://ideas.repec.org/n/nep-for/2019-04-22.html
Report NEP-FOR-2019-04-22
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:
- Hyeongwoo Kim & Kyunghwan Ko, 2019, "Improving Forecast Accuracy of Financial Vulnerability: PLS Factor Model Approach," Auburn Economics Working Paper Series, Department of Economics, Auburn University, number auwp2019-03, Apr.
- Ruijun Bu & Rodrigo Hizmeri & Marwan Izzeldin & Anthony Murphy & Mike G. Tsionas, 2019, "The Contribution of Jump Signs and Activity to Forecasting Stock Price Volatility," Working Papers, Federal Reserve Bank of Dallas, number 1902, Mar, revised 17 Dec 2022, DOI: 10.24149/wp1902r2.
- Mundt, Philipp & Alfarano, Simone & Milaković, Mishael, 2019, "Exploiting ergodicity in forecasts of corporate profitability," BERG Working Paper Series, Bamberg University, Bamberg Economic Research Group, number 147.
- Justin Sirignano & Rama Cont, 2018, "Universal features of price formation in financial markets: perspectives from Deep Learning," Working Papers, HAL, number hal-01754054, Mar.
- Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman, 2019, "Bayesian Risk Forecasting for Long Horizons," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 19-018/III, Feb.
- Stephen Bazen & Jean-Marie Cardebat, 2018, "Forecasting Bordeaux wine prices using state-space methods," Post-Print, HAL, number hal-01867216, Oct, DOI: 10.1080/00036846.2018.1472740.
- Hieu Quang Nguyen & Abdul Hasib Rahimyar & Xiaodi Wang, 2019, "Stock Forecasting using M-Band Wavelet-Based SVR and RNN-LSTMs Models," Papers, arXiv.org, number 1904.08459, Apr.
- Robin Niesert & Jochem Oorschot & Chris Veldhuisen & Kester Brons & Rutger-Jan Lange, , "Can Google Search Data Help Predict Macroeconomic Series?," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 19-021/III.
- Quast, Josefine & Wolters, Maik H., 2019, "Reliable real-time output gap estimates based on a modified Hamilton filter," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 133.
- Th'eophile Griveau-Billion & Ben Calderhead, 2019, "A Dynamic Bayesian Model for Interpretable Decompositions of Market Behaviour," Papers, arXiv.org, number 1904.08153, Apr, revised Jan 2020.