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. Jaqueson K. Galimberti issued this report. It is usually issued weekly.

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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 25/2023, Deutsche Bundesbank.
• Hanwen Xuan & Luca Maestrini & Feng Chen & Clara Grazian, 2023. "Stochastic Variational Inference for GARCH Models," Papers 2308.14952, arXiv.org.
• Webel, Karsten & Smyk, Anna, 2023. "Towards seasonal adjustment of infra-monthly time series with JDemetra+," Discussion Papers 24/2023, Deutsche Bundesbank.
• Andrii Babii & Eric Ghysels & Jonas Striaukas, 2023. "Econometrics of Machine Learning Methods in Economic Forecasting," Papers 2308.10993, arXiv.org.
• Neville Francis & Michael T. Owyang & Daniel Soques, 2023. "Impulse Response Functions for Self-Exciting Nonlinear Models," Working Papers 2023-021, Federal Reserve Bank of St. Louis, revised 29 Aug 2023.
• Damien Challet & Vincent Ragel, 2023. "Recurrent Neural Networks with more flexible memory: better predictions than rough volatility," Papers 2308.08550, arXiv.org.
• Camilo Granados & Daniel Parra-Amado, 2023. "Estimating the Output Gap After COVID: How to Address Unprecedented Macroeconomic Variations," Borradores de Economia 1249, Banco de la Republica de Colombia.