A New Approach to Forecasting the Probability of Recessions after the COVID‐19 Pandemic

Maximo Camacho, Salvador Ramallo and Manuel Ruiz

Oxford Bulletin of Economics and Statistics, 2024, vol. 86, issue 4, 833-855

Abstract: Standard recession forecasting based on economic indicators has become unsettled due to COVID‐19 pandemic's limited but influential data. This paper proposes a new non‐parametric approach to computing predictive probabilities of future recessions that is robust to influential observations and other data irregularities. The method simulates forecasts using past data histories embedded into a symbolic space. Then, the forecasts are converted into probability statements, which are weighted by the forecast probabilities of their respective symbols. Using GDP data from G7, our proposal outperforms other parametric approaches in classifying future national business cycle phases, especially including data from 2020 in the sample.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1111/obes.12616

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:bla:obuest:v:86:y:2024:i:4:p:833-855

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=0305-9049

Access Statistics for this article

Oxford Bulletin of Economics and Statistics is currently edited by Christopher Adam, Anindya Banerjee, Christopher Bowdler, David Hendry, Adriaan Kalwij, John Knight and Jonathan Temple

More articles in Oxford Bulletin of Economics and Statistics from Department of Economics, University of Oxford Contact information at EDIRC.
Bibliographic data for series maintained by Wiley Content Delivery ().