Study Results from Federal Reserve Bank of Cleveland Provide New Insights into Investment (Specification Choices In Quantile Regression for Empirical Macroeconomics): Investment

2024 DEC 11 (NewsRx) -- By a News Reporter-Staff News Editor at Economics Daily Report -- Data detailed on Investment have been presented. According to news reporting out of Cleveland, Ohio, by NewsRx editors, research stated, “Quantile regression has become widely used in empirical macroeconomics, in particular for estimating and forecasting tail risks.”

Our news journalists obtained a quote from the research from the Federal Reserve Bank of Cleveland, “This paper examines various choices in the specification of quantile regressions for macro applications, including how and to what extent to include shrinkage and whether to apply shrinkage in a classical or Bayesian framework. We focus on forecasting accuracy, measured with quantile scores and quantile-weighted continuous ranked probability scores at a range of quantiles from the left to right tail.”

According to the news editors, the research concluded: “Across applications, we find that shrinkage is generally helpful to quantile forecast accuracy, with Bayesian quantile regression dominating frequentist quantile regression.JEL Classification: C53, E17, E37, F47.”

This research has been peer-reviewed.

For more information on this research see: Specification Choices In Quantile Regression for Empirical Macroeconomics. Journal of Applied Econometrics, 2024. Journal of Applied Econometrics can be contacted at: Wiley, 111 River St, Hoboken 07030-5774, NJ, USA. (Wiley-Blackwell - http://www.wiley.com/; Journal of Applied Econometrics - http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-1255)

Our news journalists report that additional information may be obtained by contacting Todd E. Clark, Federal Reserve Bank of Cleveland, Dept. of Research, Cleveland, OH 44114, United States. Additional authors for this research include Andrea Carriero and Massimiliano Marcellino.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1002/jae.3099. 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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