Printed from https://ideas.repec.org/n/nep-ets/2021-02-08.html
Report NEP-ETS-2021-02-08
This is the archive for NEP-ETS, a report on new working papers in the area of Econometric Time Series. Yong Yin issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-ETS
The following items were announced in this report:
- Palumbo, D., 2021, "Testing and Modelling Time Series with Time Varying Tails," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2111, Jan.
- Paulo M.M. Rodrigues & Marina Balboa, 2021, "Multivariate Fractional Integration Tests allowing for Conditional Heteroskedasticity with an Application to Return Volatility and Trading Volume," Working Papers, Banco de Portugal, Economics and Research Department, number w202102.
- Pablo Montero-Manso & Rob J Hyndman, 2020, "Principles and Algorithms for Forecasting Groups of Time Series: Locality and Globality," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 45/20.
- Ken Chung & Anthony Bellotti, 2021, "Evidence and Behaviour of Support and Resistance Levels in Financial Time Series," Papers, arXiv.org, number 2101.07410, Jan.
- Pincheira, Pablo & Hardy, Nicolás & Muñoz, Felipe, 2021, ""Go wild for a while!": A new asymptotically Normal test for forecast evaluation in nested models," MPRA Paper, University Library of Munich, Germany, number 105368, Jan.
- Racine Ly & Fousseini Traore & Khadim Dia, 2021, "Forecasting Commodity Prices Using Long Short-Term Memory Neural Networks," Papers, arXiv.org, number 2101.03087, Jan, revised Jan 2021.
- Leonard Goke & Mario Kendziorski, 2021, "Adequacy of time-series reduction for renewable energy systems," Papers, arXiv.org, number 2101.06221, Jan, revised Aug 2021.
- Florian Eckert & Philipp Kronenberg & Heiner Mikosch & Stefan Neuwirth, 2020, "Tracking Economic Activity With Alternative High-Frequency Data," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 20-488, Dec, DOI: 10.3929/ethz-b-000458723.
- Fajar, Muhammad & Prasetyo, Octavia Rizky & Nonalisa, Septiarida & Wahyudi, Wahyudi, 2020, "Forecasting unemployment rate in the time of COVID-19 pandemic using Google trends data (case of Indonesia)," MPRA Paper, University Library of Munich, Germany, number 105042, Nov, revised 30 Nov 2020.