A1625
Title: Tuning-free testing of factor regression against factor-augmented sparse alternatives
Authors: Jad Beyhum - KU Leuven (Belgium) [presenting]
Jonas Striaukas - Copenhagen Business School (Denmark)
Abstract: The purpose is to introduce 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 suitability of the classical dense factor regression model compared to a sparse plus dense alternative augmenting factor regression with idiosyncratic shocks. The 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 theoretically established under time-series dependence. Through simulation experiments, the favourable finite sample performance of the procedure is demonstrated. Moreover, using the FRED-MD dataset, the test is applied and the adequacy of the classical factor regression model is rejected when the dependent variable is inflation but not industrial production. These findings offer insights into selecting appropriate models for high-dimensional datasets.
