type="main" xml:id="obes12052-abs-0001" <title type="main">Abstract</title> <p>In this article, we try to realize the best compromise between in-sample goodness of fit and out-of-sample predictability of sovereign defaults. To do this, we use a new regression-tree based approach that signals impending sovereign debt crises...</p>

This paper employs a recent statistical algorithm (CRAGGING) in order to build an early warning model for banking crises in emerging markets. We perturb our data set many times and create “artificial” samples from which we estimated our model, so that, by construction, it is flexible enough...

In this paper we face the fitting versus forecasting paradox with the objective of realizing an optimal Early Warning System to better describe and predict past and future sovereign defaults. We do this by proposing a new Regression Tree-based model that signals a potential crisis whenever...

This paper focuses on predictability of sovereign debt crisis proposing a two-step procedure centered on the idea of a multidimensional distance-to-collapse point. The first step is non-parametric and devoted to constructing a generalized early warning system that signals a potential crisis. The...

We propose a methodology for modeling convergence in the presence of transitional dynamics. We explore the dynamic behavior of the difference between two series by allowing the parameters to change across time without imposing any formulation restrictions, using a threshold approach. We adopt an...

We develop threshold models that allow volatilities and copula functions or their association parameters to change across time. The number and location of the thresholds is assumed unknown. We use a Markov chain Monte Carlo strategy combined with Laplace estimates that evaluate the required...