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Report NEP-ETS-2023-10-02
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:
- Mertens, Elmar, 2023, "Precision-based sampling for state space models that have no measurement error," Discussion Papers, Deutsche Bundesbank, number 25/2023.
- Hanwen Xuan & Luca Maestrini & Feng Chen & Clara Grazian, 2023, "Stochastic Variational Inference for GARCH Models," Papers, arXiv.org, number 2308.14952, Aug.
- Webel, Karsten & Smyk, Anna, 2023, "Towards seasonal adjustment of infra-monthly time series with JDemetra+," Discussion Papers, Deutsche Bundesbank, number 24/2023.
- Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023, "Econometrics of Machine Learning Methods in Economic Forecasting," Papers, arXiv.org, number 2308.10993, Aug.
- Neville Francis & Michael T. Owyang & Daniel Soques, 2023, "Impulse Response Functions for Self-Exciting Nonlinear Models," Working Papers, Federal Reserve Bank of St. Louis, number 2023-021, Aug, revised 29 Aug 2023, DOI: 10.20955/wp.2023.021.
- Damien Challet & Vincent Ragel, 2023, "Recurrent Neural Networks with more flexible memory: better predictions than rough volatility," Papers, arXiv.org, number 2308.08550, Aug.
- Camilo Granados & Daniel Parra-Amado, 2023, "Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations," Borradores de Economia, Banco de la Republica de Colombia, number 1249, Sep, DOI: 10.32468/be.1249.