Unraveling News: Reconciling Conflicting Evidence

Abstract

Abstract This paper addresses the lack of consensus in the empirical literature regarding the effects of technology diffusion news shocks. We attribute the conflicting evidence to the wide diversity in terms of variable settings, productivity series used, and identification schemes applied. We analyze the different identification schemes that have been employed in this literature. More specifically, we impose short- and medium-run restrictions to identify a news shock. The focus is on the medium-run identification maximizing at and over different horizons. We show that the identified news shock depends critically on the applied identification scheme and on the maximization horizon. We also investigate the importance of the information content of the model and of the productivity measure used. We find that models which either contain a large set of macroeconomic variables or include variables that are strongly forward looking deliver more robust results. Moreover, we show that the productivity series used may influence results, but there is convergence of findings for newer total factor productivity series vintages. Our conclusion is that news shocks have expansionary properties.