Testing for sparse idiosyncratic components in factor-augmented regression models

Jad Beyhum and Jonas Striaukas

Journal of Econometrics, 2024, vol. 244, issue 1

Abstract: We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative model augmented 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.

Keywords: Sparse plus dense; High-dimensional inference; LASSO; Factor models (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:244:y:2024:i:1:s0304407624001908

DOI: 10.1016/j.jeconom.2024.105845

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