Predicting scalar outcomes by using functional predictors is a classical problem in functional data analysis. In many applications, however, only specific locations or time points of the functional predictors have an influence on the outcome. Such ‘points of impact’ are typically unknown and...

This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques— the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a...

We analyse the effect of a mandatory kindergarten for four-year-old children on maternal labour supply in Switzerland by using two quasi-experiments: Firstly, we use a large administrative dataset and apply a non-parametric Regression Discontinuity Design to evaluate the effect of the reform at...

This study uses machine learning techniques to identify the key drivers of financial development in Africa. To this end, four regularization techniques- the Standard lasso, Adaptive lasso, the minimum Schwarz Bayesian information criterion lasso, and the Elasticnet are trained based on a dataset...

This paper is concerned with the problem of variable selection when the marginal effects of signals on the target variable as well as the correlation of the covariates in the active set are allowed to vary over time, without committing to any particular model of parameter instabilities. It poses...

We represent the dynamic relation among variables in vector autoregressive (VAR) models as directed graphs. Based on these graphs, we identify so‐called strongly connected components. Using this graphical representation, we consider the problem of variable choice. We use the relations among...

The Mzansi intervention is a major initiative designed to provide banking services to the unbanked South African population. This study investigates the underlying variables that define the choice of a Mzansi account from a consumer perspective. Unlike previous studies, we do not assume that...

This study considers Bayesian variable selection in the Phillips curve context by using the Bernoulli approach of Korobilis (2013a). The Bernoulli model, however, is unable to account for model change over time, which is important if the set of relevant predictors changes over time. To tackle...

This paper considers factor estimation from heterogenous data, where some of the variables are noisy and only weakly informative for the factors. To identify the irrelevant variables, we search for zero rows in the loadings matrix of the factor model. To sharply separate these irrelevant...

A general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate dependent mixture weights. The four parameters of the...