Bias Correction in Factor-Augmented Regression Models with Weak Factors

Abstract: In this paper, we study the asymptotic bias of the factor-augmented regression estimator and its reduction, which is augmented by the $r$ factors extracted from a large number of $N$ variables with $T$ observations. In particular, we consider general weak latent factor models with $r$ signal eigenvalues that may diverge at different rates, $N^{\alpha {k}}$, $0<\alpha _{k}\leq 1$, $k=1,\dots,r$. In the existing literature, the bias has been derived using an approximation for the estimated factors with a specific data-dependent rotation matrix $\hat{H}$ for the model with $\alpha{k}=1$ for all $k$, whereas we derive the bias for weak factor models. In addition, we derive the bias using the approximation with a different rotation matrix $\hat{H}_q$, which generally has a smaller bias than with $\hat{H}$. We also derive the bias using our preferred approximation with a purely signal-dependent rotation $H$, which is unique and can be regarded as the population version of $\hat{H}$ and $\hat{H}_q$. Since this bias is parametrically inestimable, we propose a split-panel jackknife bias correction, and theory shows that it successfully reduces the bias. The extensive finite-sample experiments suggest that the proposed bias correction works very well, and the empirical application illustrates its usefulness in practice.