Simula@BI seminar: Jonas Striaukas, Copenhagen Business School

Abstract

This study introduces a bootstrap test of the validity of factor regression within a high-dimensional factor-augmented sparse regression model that integrates factor and sparse regression techniques. The test provides a means to assess the suitabil- ity of the classical dense factor regression model compared to a sparse plus dense alternative augmenting factor regression with idiosyncratic shocks. Our proposed test does not require tuning parameters, eliminates the need to estimate covariance matrices, and offers simplicity in implementation. The validity of the test is theo- retically established under time-series dependence. Through simulation experiments, we demonstrate the favorable finite sample performance of our procedure. Moreover, using the FRED-MD dataset, we apply the test and reject the adequacy of the classical factor regression model when the dependent variable is inflation but not when it is industrial production. These findings offer insights into selecting appropriate models for high-dimensional datasets.

About the speaker

Striaukas is an assistant professor of statistics and finance and Marie Skłodowska-Curie Action fellow at the Copenhagen Business School, Department of Finance. His main research interests are econometrics/statistics and applications of machine learning methods to financial and macro econometrics. 

• Read the paper: Tuning-free testing of factor regression against factor-augmented sparse alternatives (pdf)
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