Report NEP-FOR-2020-05-11
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:
- Sarthak Behera & Hyeongwoo Kim & Soohyon Kim, 2020. "Forecasting the US Dollar-Korean Won Exchange Rate: A Factor-Augmented Model Approach," Auburn Economics Working Paper Series auwp2020-02, Department of Economics, Auburn University.
- John H. Rogers & Jiawen Xu, 2019. "How Well Does Economic Uncertainty Forecast Economic Activity?," Finance and Economics Discussion Series 2019-085, Board of Governors of the Federal Reserve System (U.S.).
- Michael Cai & Marco Del Negro & Edward P. Herbst & Ethan Matlin & Reca Sarfati & Frank Schorfheide, 2020. "Online Estimation of DSGE Models," Finance and Economics Discussion Series 2020-023, Board of Governors of the Federal Reserve System (U.S.).
- Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
- Marc-Oliver Pohle, 2020. "The Murphy Decomposition and the Calibration-Resolution Principle: A New Perspective on Forecast Evaluation," Papers 2005.01835, arXiv.org.
- Gaetano Perone, 2020. "An ARIMA model to forecast the spread and the final size of COVID-2019 epidemic in Italy," Health, Econometrics and Data Group (HEDG) Working Papers 20/07, HEDG, c/o Department of Economics, University of York.
- Manuel Nunes & Enrico Gerding & Frank McGroarty & Mahesan Niranjan, 2020. "Long short-term memory networks and laglasso for bond yield forecasting: Peeping inside the black box," Papers 2005.02217, arXiv.org.
- Sidra Mehtab & Jaydip Sen, 2020. "A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models," Papers 2004.11697, arXiv.org, revised May 2021.
- Theresa Kuchler & Dominic Russel & Johannes Stroebel, 2020. "The Geographic Spread of Covid-19 Correlates with Structure of Social Networks as Measured by Facebook," CESifo Working Paper Series 8241, CESifo.
- Bakens, Jessie & Fouarge, Didier & Goedhart, Rogier, 2020. "Labour market forecasts by education and occupation up to 2024," ROA Technical Report 002, Maastricht University, Research Centre for Education and the Labour Market (ROA).