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Report NEP-ETS-2021-12-06
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:
- Donggyu Kim, 2021, "Exponential GARCH-Ito Volatility Models," Papers, arXiv.org, number 2111.04267, Nov.
- Gianluca Cubadda & Alain Hecq, 2021, "Reduced Rank Regression Models in Economics and Finance," CEIS Research Paper, Tor Vergata University, CEIS, number 525, Nov, revised 08 Nov 2021.
- Jeronymo Marcondes Pinto & Jennifer L. Castle, 2021, "A machine learning dynamic switching approach to forecasting when there are structural breaks," Economics Series Working Papers, University of Oxford, Department of Economics, number 950 JEL classification: C, Oct.
- Carlos A. Abanto-Valle & Gabriel Rodríguez & Luis M. Castro Cepero & Hernán B. Garrafa-Aragón, 2021, "Approximate Bayesian Estimation of Stochastic Volatility in Mean Models using Hidden Markov Models: Empirical Evidence from Stock Latin American Markets," Documentos de Trabajo / Working Papers, Departamento de Economía - Pontificia Universidad Católica del Perú, number 2021-502, DOI: 10.18800/2079-8474.0502.
- Florian Eckert & Nina Mühlebach, 2021, "Global and Local Components of Output Gaps," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 21-497, Nov, DOI: 10.3929/ethz-b-000514977.
- Sattarhoff, Cristina & Lux, Thomas, 2021, "Forecasting the Variability of Stock Index Returns with the Multifractal Random Walk Model for Realized Volatilities," Economics Working Papers, Christian-Albrechts-University of Kiel, Department of Economics, number 2021-02.
- Sayani Gupta & Rob J Hyndman & Dianne Cook, 2021, "Detecting Distributional Differences between Temporal Granularities for Exploratory Time Series Analysis," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 20/21.