Two new working papers on factor-augmented sparse regressions - check them out
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).
The paper introduces structured machine learning regressions for heavy-tailed dependent panel data potentially sampled at differentrent frequencies. We focus on the sparse-group LASSO regularization. This type of regularization can take advantage of the mixed frequency time series panel data structures and improve the quality of the estimates. We obtain oracle inequalities for the pooled and fixed effects sparse-group LASSO panel data estimators recognizing that financial and economic data can have fat tails. To that end, we leverage on a new Fuk-Nagaev concentration inequality for panel data consisting of heavy-tailed τ-mixing processes. Lastly, we study the HAC estimator of the long-run variance based on the sparse-group LASSO residuals for pooled panel regression. Therefore, we provide a valid inference method individual regression coefficients as well as groups, including Granger causality tests, for high-dimensional pooled panel regressions.
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.