A paper on testing the sparse component within the factor-augmented sparse regressions is forthcoming at Journal of Econometrics - check it out
here!
Welcome!
I am an assistant professor of statistics and finance and Marie Skłodowska-Curie Action fellow at the Copenhagen Business School, Department of Finance. My main research interests are econometrics/statistics and applications of machine learning methods to financial and macro econometrics.
Before joining the Copenhagen Business School in 2022, I was a research fellow at the Fonds de la Recherche Scientifique—FNRS (National Fund for Scientific Research in Belgium) and Université Catholique de Louvain, where I carried out my PhD under the supervision of prof. Andrii Babii (UNC Chapel Hill) and prof. Eric Ghysels (UNC Chapel Hill).
We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmented model with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoretical validity. In simulation experiments, our procedure exhibits high power against sparse alternatives and low power against dense deviations from the null. Moreover, we apply our test to various datasets in macroeconomics and finance and often reject the null. This suggests the presence of sparsity — on top of a dense component — in commonly studied economic applications. The R package 'FAS' implements our approach.
This paper introduces structured machine learning regressions for high-dimensional time series data potentially sampled at different frequencies. The sparse-group LASSO estimator can take advantage of such time series data structures and outperforms the unstructured LASSO. We establish oracle inequalities for the sparse-group LASSO estimator within a framework that allows for the mixing processes and recognizes that the financial and the macroeconomic data may have heavier than exponential tails. An empirical application to nowcasting US GDP growth indicates that the estimator performs favorably compared to other alternatives and that text data can be a useful addition to more traditional numerical data.