In his new paper Ignace De Vos introduces the cross‑section bootstrap method for more reliable inference with Common Correlated Effects (CCE) regressions in macroeconomic settings.
Ignace De Vos and his co-author Ovidijus Stauskas show that while the pooled CCE (CCEP) estimator is popular for controlling the effects of unobserved components—even when variables exhibit different orders of integration—it suffers from a disruptive asymptotic bias in typical macroeconomic panel settings. Their study establishes that the cross section bootstrap successfully replicates the distribution of CCE estimators, enabling straightforward bias correction and valid confidence intervals under very general factor structures. This broadens the method’s applicability in empirical macroeconomic analysis, where unobserved stationary and non stationary components often coexist, without knowledge by the researcher. A key benefit is that the users do not need to be aware of whether the unobserved components are stationary or not.
The paper is titled Cross-Section Bootstrap for CCE Regressions with General Unknown Factors and is available online.