Forecasting banking crises with dynamic panel probit models

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Banking crises are rare events, but when they occur, their consequences are often dramatic. The aim of this paper is to contribute to the toolkit of early warning models that is available to policy makers by exploring the dynamics and exuberances embedded in a panel dataset that covers 22 European countries over four decades (from 1970Q1 to 2012Q4). The in- and out-of-sample forecast performances of several (dynamic) probit models are evaluated, with the objective of developing common vulnerability indicators with early warning properties. The results obtained show that adding dynamic components and exuberance indicators to the models improves the performances of early warning models significantly.

Original languageEnglish
Pages (from-to)249-275
Number of pages27
JournalInternational Journal of Forecasting
Volume34
Issue number2
DOIs
Publication statusPublished - 1 Apr 2018

Fingerprint

Early warning
Banking crisis
Probit model
Dynamic panel
Toolkit
European countries
Rare events
Forecast performance
Politicians
Out-of-sample forecasting
Vulnerability

Keywords

  • Banking crisis
  • Binary data
  • Dynamic probit models
  • Early warning indicators

Cite this

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title = "Forecasting banking crises with dynamic panel probit models",
abstract = "Banking crises are rare events, but when they occur, their consequences are often dramatic. The aim of this paper is to contribute to the toolkit of early warning models that is available to policy makers by exploring the dynamics and exuberances embedded in a panel dataset that covers 22 European countries over four decades (from 1970Q1 to 2012Q4). The in- and out-of-sample forecast performances of several (dynamic) probit models are evaluated, with the objective of developing common vulnerability indicators with early warning properties. The results obtained show that adding dynamic components and exuberance indicators to the models improves the performances of early warning models significantly.",
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Forecasting banking crises with dynamic panel probit models. / Antunes, António; Bonfim, Diana; Monteiro, Nuno; Rodrigues, Paulo M.M.

In: International Journal of Forecasting, Vol. 34, No. 2, 01.04.2018, p. 249-275.

Research output: Contribution to journalArticle

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