Statistical Learning refers to statistical aspects of automated extraction of regularities (structure) in datasets. It is a broad area which includes neural networks, regression-trees, nonparametric statistics and sieve approximation, boosting, mixtures of models, computational complexity,...

In this paper, we propose a computationally effective approach to detect multiple structural breaks in the mean occurring at unknown dates. We present a non-parametric approach that exploits, in the framework of least squares regression trees, the contiguity property of data generating processes...

In this paper, we realise an early warning system for hedge funds based on specific red flags that help detect the symptoms of impending extreme negative returns and the contagion effect. To do this we use regression tree analysis to identify a series of splitting rules that act as risk signals....

This paper estimates the causal impact of investment in information andcommunication technologies (ICT) on student performances in mathematics asmeasured in the Program for International Student Assessment (PISA) 2012 for Spain.To do this we apply a new methodology in this context known as...

In this paper, we examine the stock exchange index returns for the panel of 27 EU countries in the last ten years. Our method of choice is a special kind of tree models, namely model baþed recursive partitioning (MOB). The MOB algorithm uses the generalized M-fluctuation test to examine the...

We investigate the effects of political institutions on economic growth. We specifically explore this relationship while controlling for heterogeneity and model uncertainty. We use threshold regression (Hansen (2000)) to search for possible nonlinearities and/or interaction effects with respect...

This paper studies categorizations that are optimal for the purpose of making predictions. A subject encounters an object (x,y). She observes the first component, x, and has to predict the second component, y. The space of objects is partitioned into categories. The subject determines what...

We use a machine-learning approach known as Boosted Regression Trees (BRT) to reexamine the usefulness of selected leading indicators for predicting recessions. We estimate the BRT approach on German data and study the relative importance of the indicators and their marginal effects on the...

The paper analyzes factors that influence the adoption of e-learning and gives an example of how to forecast technology adoption based on a post-hoc predictive segmentation using a classification and regression tree (CART). We find strong evidence for the existence of technological...