Recession forecasting using Bayesian classification

Troy Davig and Aaron Smalter Hall

No RWP 16-6, Research Working Paper from Federal Reserve Bank of Kansas City

Abstract: The authors demonstrated the use of a Nave Bayes model as a recession forecasting tool. The approach has a close connection to Markov-switching models and logistic regression but also important differences. In contrast to Markov-switching models, Nave Bayes treats National Bureau of Economic Research business cycle turning points as data rather than hidden states to be inferred by the model. Although Nave Bayes and logistic regression are asymptotically equivalent under certain distributional assumptions, the assumptions do not hold for business cycle data.

Keywords: Forecasting; Naïve Bayes model; Recession (search for similar items in EconPapers)
JEL-codes: C11 C5 E32 E37 (search for similar items in EconPapers)
Pages: 37 pages
Date: 2016-08-01
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.kansascityfed.org/documents/505/pdf-Re ... 20Classification.pdf (application/pdf)
Our link check indicates that this URL is bad, the error code is: 404 Not Found

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedkrw:rwp16-06

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Research Working Paper from Federal Reserve Bank of Kansas City Contact information at EDIRC.
Bibliographic data for series maintained by Zach Kastens ().