Report NEP-FOR-2019-11-04
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.
Other reports in NEP-FOR
The following items were announced in this report:
- Tae-Hwy Lee & Eric Hillebrand & Huiyu Huang & Canlin Li, 2018. "Using the Entire Yield Curve in Forecasting Output and Inflation," Working Papers 201903, University of California at Riverside, Department of Economics.
- Tae-Hwy Lee & Yundong Tu, 2018. "Forecasting Using Supervised Factor Models," Working Papers 201909, University of California at Riverside, Department of Economics.
- Gergely Ganics & Florens Odendahl, 2019. "Bayesian VAR Forecasts, Survey Information and Structural Change in the Euro Area," Working papers 733, Banque de France.
- Mawuli Segnon & Rangan Gupta & Keagile Lesame & Mark E. Wohar, 2019. "High-Frequency Volatility Forecasting of US Housing Markets," Working Papers 201977, University of Pretoria, Department of Economics.
- Tae-Hwy Lee & Bai Huang & Aman Ullah, 2018. "A Combined Random Effect and Fixed Effect Forecast for Panel Data Models," Working Papers 201906, University of California at Riverside, Department of Economics.
- Kai Carstensen & Markus Heinrich & Magnus Reif & Maik H. Wolters, 2019. "Predicting Ordinary and Severe Recessions with a Three-State Markov-Switching Dynamic Factor Model," Jena Economics Research Papers 2019-006, Friedrich-Schiller-University Jena.
- González-Rivera, Gloria & Luo, Yun, 2019. "Prediction regions for interval-valued time series," DES - Working Papers. Statistics and Econometrics. WS 29054, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Gloria Gonzalez-Rivera & Yun Luo & Esther Ruiz, 2019. "Prediction Regions for Interval-valued Time Series," Working Papers 201921, University of California at Riverside, Department of Economics.
- Timo Dimitriadis & Andrew J. Patton & Patrick W. Schmidt, 2019. "Testing Forecast Rationality for Measures of Central Tendency," Papers 1910.12545, arXiv.org, revised Jul 2024.
- MORIKAWA Masayuki, 2019. "Uncertainty in Long-Term Macroeconomic Forecasts: Ex post Evaluation of Forecasts by Economics Researchers," Discussion papers 19084, Research Institute of Economy, Trade and Industry (RIETI).
- MORIKAWA Masayuki, 2019. "Uncertainty in Long-Term Economic Forecasts (Japanese)," Discussion Papers (Japanese) 19058, Research Institute of Economy, Trade and Industry (RIETI).
- Ooft, G. & Bhaghoe, S. & Franses, Ph.H.B.F., 2019. "Forecasting Annual Inflation in Suriname," Econometric Institute Research Papers EI2019-32, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
- Domenico Delli Gatti & Jakob Grazzini, 2019. "Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models," CESifo Working Paper Series 7894, CESifo.
- Halbleib, Roxana & Dimitriadis, Timo, 2019. "How informative is high-frequency data for tail risk estimation and forecasting? An intrinsic time perspectice," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203669, Verein für Socialpolitik / German Economic Association.
- Hinterlang, Natascha, 2019. "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy 203503, Verein für Socialpolitik / German Economic Association.
- Michael Coelli & Jeff Borland, 2019. "Behind the headline number: Why not to rely on Frey and Osborne’s predictions of potential job loss from automation," Melbourne Institute Working Paper Series wp2019n10, Melbourne Institute of Applied Economic and Social Research, The University of Melbourne.
- Tae-Hwy Lee & Yiyao Wang, 2018. "Evaluation of the Survey of Professional Forecasters in the Greenbook’s Loss Function," Working Papers 201904, University of California at Riverside, Department of Economics.