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Penalized maximum likelihood estimation of logit-based early warning systems
International Journal of Forecasting ( IF 7.022 ) Pub Date : 2021-02-04 , DOI: 10.1016/j.ijforecast.2021.01.004
Claudia Pigini

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-specific 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, improves the predictive performance of ews, both in simulation and out of sample, with respect to the pooled, random-effects and standard fixed-effects models.



中文翻译:

基于Logit的预警系统的惩罚最大似然估计

事实证明,面板logit模型是建立针对金融危机的预警系统(ews)的简单有效的工具。但是由于危机是罕见事件,因此对母羊的估计通常不会考虑特定国家的固定影响,从而避免丢失与从未面临危机的国家有关的所有信息。我建议对保留所有观测值的基于固定结果对数的母羊使用惩罚最大似然估计器。我表明,在保留整个样本的同时,包括国家效应在内,相对于汇总,随机效应和标准固定效应模型,无论是模拟还是样本外,母羊的预测性能都得到了改善。

更新日期:2021-02-04
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