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PENALIZED MAXIMUM LIKELIHOOD ESTIMATION OF LOGIT-BASED EARLY WARNING SYSTEMS

Claudia Pigini

No 441, Working Papers from Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali

Abstract: Panel logit models have proved to be simple and effective tools to build Early Warning Systems (EWS) for financial crises. But because crises are rare events, the estimation of EWS does not usually account for country fixed effects, so as to avoid losing all the information relative to countries that never face a crisis. I propose using a penalized maximum likelihood estimator for fixed-effects logit-based EWS where all the observations are retained. I show that including country effects, while preserving the entire sample, greatly improves the predictive power of EWS with respect to the pooled, random-effects and standard fixed-effects models.

Keywords: Keywords: Banking Crisis; Bias Reduction; Fixed-Effects Logit; Separated Data (search for similar items in EconPapers)
JEL-codes: C23 C25 G17 G21 (search for similar items in EconPapers)
Pages: 33
Date: 2019-11
New Economics Papers: this item is included in nep-dcm and nep-ecm
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http://docs.dises.univpm.it/web/quaderni/pdf/441.pdf First version, 2019 (application/pdf)

Related works:
Journal Article: Penalized maximum likelihood estimation of logit-based early warning systems (2021) Downloads
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