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

Bibliographic Data

ID21512769
AuthorsMáximo Camacho (0000-0003-3158-5614, Department of Quantitative Methods for Economics and Business Universidad de Murcia Murcia 30100 Spain, corresponding author), Salvador Ramallo (0000-0001-6239-1957, Department of Quantitative Methods for Economics and Business Universidad de Murcia Murcia 30100 Spain), M Rosario Marín (0000-0001-9228-6410, Universidad Politécnica de Cartagena), Manuel Ruiz (0000-0001-8153-276X, Department of Quantitative Methods Universidad Politécnica de Cartagena, Law and Modern Languages Cartagena 30201 Spain)
Year2024
Volume86
Issue4
Pages833-855
Publication date2024-08-01
Open AccessYes
TypeARTICLE
VenueOxford Bulletin of Economics and Statistics (JOURNAL)
Journal identifiersISSN: 0305-9049 • E-ISSN: 1468-0084
PublisherWiley (PUBLISHER • GB)
DOI10.1111/obes.12616
OpenAlexW4396896161
LanguageEN
References cited23

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

2019-20 coronavirus outbreak · Econometrics · Economics · Keynesian economics · Outbreak · Pandemic · Recession · Financial Risk and Volatility Modeling · Market Dynamics and Volatility · Medicine · Monetary Policy and Economic Impact · Internal Medicine · Virology

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Open DOIOpen Access