Federal Reserve Bank of Cleveland Reports Findings in COVID-19 (Addressing Covid-19 Outliers In Bvars With Stochastic Volatility): Coronavirus – COVID-19

2024 OCT 08 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx COVID-19 Daily -- Investigators publish new report on Coronavirus - COVID-19. According to news originating from Cleveland, Ohio, by NewsRx correspondents, research stated, “The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard Bayesian vector autoregressions (BVARs). To address these issues, we propose BVAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility.”

Financial support for this research came from Ministry of Education, Universities and Research (MIUR).

Our news journalists obtained a quote from the research from the Federal Reserve Bank of Cleveland, “The resulting density forecasts are much less sensitive to outliers in the data than standard BVARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best fit for the pandemic period, as well as for earlier subsamples of high volatility.”

According to the news editors, the research concluded: “In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.”

This research has been peer-reviewed.

For more information on this research see: Addressing Covid-19 Outliers In Bvars With Stochastic Volatility. Review of Economics and Statistics, 2024;106(5):1403-1417. Review of Economics and Statistics can be contacted at: Mit Press, One Rogers St, Cambridge, MA 02142-1209, USA.

The news correspondents report that additional information may be obtained from Todd E. Clark, Federal Reserve Bank of Cleveland, Cleveland, OH 44114, United States. Additional authors for this research include Andrea Carriero, Massimiliano Marcellino and Elmar Mertens.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1162/rest_a_01213. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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