Benchmarking econometric and machine learning methodologies in nowcasting GDP
Author:
Publisher
Springer Science and Business Media LLC
Subject
Economics and Econometrics,Social Sciences (miscellaneous),Mathematics (miscellaneous),Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s00181-023-02515-6.pdf
Reference80 articles.
1. Ankargren S, Yang Y, Kastner G (2021). Package ’mfbvar’. https://cran.r-project.org/web/packages/mfbvar/mfbvar.pdf
2. Antolin-Diaz J, Drechsel T, Petrella I (2020) Advances in nowcasting economic activity: secular trends, large shocks and new data. SSRN. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3669854
3. Babii A, Ghysels E, Striaukas J (2022) Machine learning time series regressions with an application to nowcasting. J Bus Econ Stat 40:1094–1106. https://doi.org/10.1080/07350015.2021.1899933
4. Bańbura M, Rünstler G (2011) A look into the factor model black box: Publication lags and the role of hard and soft data in forecasting GDP. Int J Forecast 27:333–346. https://doi.org/10.1016/j.ijforecast.2010.01.011
5. Bańbura M, Giannone D, Reichlin L (2010) Large Bayesian vector auto regressions. J Appl Economet 25:71–92. https://doi.org/10.1002/jae.1137
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