Testing for sparse idiosyncratic components in factor-augmented regression models

Jad Beyhum and Jonas Striaukas

Papers from arXiv.org

Abstract: We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmenting 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 model -- in commonly studied economic applications. The R package FAS implements our approach.

Date: 2023-07, Revised 2024-07
New Economics Papers: this item is included in nep-ecm and nep-ets
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