Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/314210 
Year of Publication: 
2022
Citation: 
[Journal:] Journal of Applied Economics [ISSN:] 1667-6726 [Volume:] 25 [Issue:] 1 [Year:] 2022 [Pages:] 477-503
Publisher: 
Taylor & Francis, Abingdon
Abstract: 
This paper applies a Qual VAR approach to generate a continuous banking crisis indicator from an underlying latent variable using a Markov Chain Monte Carlo algorithm. Four decades of banking crises are assessed by accounting for the evolutionary nature of precursors, as measured through periodic, regional, and developmental effects using a representative sample of countries. Aggregate results from forecast error variance decomposition show that banking sector variables explain nearly half of total variation, external sector a third and real sector a fifth. Findings suggest that recursive out-of-sample forecasts up to 12-months preceding a banking crisis render vital early warning signals, and as based on quarterly data, support expeditious response times. In out-of-sample forecasting, the Qual VAR outperforms a probit model. Improved forecasting performance may assist banking oversight departments and support remediation efforts of policymakers to adequately and timeously respond to banking crises.
Subjects: 
banking crises
early warning signal
forecasting
latent variable
leading indicators
Markov Chain Monte Carlo
Qual VAR
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article

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