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Report NEP-ETS-2017-11-26
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:
- Gregor Kastner & Florian Huber, 2017, "Sparse Bayesian vector autoregressions in huge dimensions," Papers, arXiv.org, number 1704.03239, Apr, revised Dec 2019.
- Ariel Navon & Yosi Keller, 2017, "Financial Time Series Prediction Using Deep Learning," Papers, arXiv.org, number 1711.04174, Nov.
- Florian Huber & Gregor Kastner & Martin Feldkircher, 2016, "Should I stay or should I go? A latent threshold approach to large-scale mixture innovation models," Papers, arXiv.org, number 1607.04532, Jul, revised Jul 2018.
- Matteo Barigozzi & Matteo Luciani, 2017, "Common Factors, Trends, and Cycles in Large Datasets," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2017-111, Nov, DOI: 10.17016/FEDS.2017.111.
- Alexander Chudik & M. Hashem Pesaran, 2017, "An Augmented Anderson-Hsiao Estimator for Dynamic Short-T Panels," Globalization Institute Working Papers, Federal Reserve Bank of Dallas, number 327, Sep, revised 27 Mar 2021, DOI: 10.24149/gwp327r2.
- Franses, Ph.H.B.F. & Janssens, E., 2017, "Spurious Principal Components," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number EI2017-31, Nov.