Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
This paper reconciles two widely used trend–cycle decompositions of GDP that give markedly different estimates: the correlated unobserved components model yields output gaps that are small in amplitude, whereas the Hodrick–Prescott (HP) filter generates large and persistent cycles. By embedding the HP filter in an unobserved components model, we show that this difference arises due to differences in the way the stochastic trend is modeled. Moreover, the HP filter implies that the cyclical components are serially independent—an assumption that is decidedly rejected by the data. By relaxing this restrictive assumption, the augmented HP filter provides comparable model fit relative to the standard correlated unobserved components model.