Information in the Revision Process of Real-Time Datasets

Abstract

Rationality of early release data is typically tested using linear regressions. Thus, failure to reject the null does not rule out the possibility of nonlinear dependence. This paper proposes two tests which instead have power against generic nonlinear alternatives. A Monte Carlo study shows that the suggested tests have good finite sample properties. Additionally, we carry out an empirical illustration using a real-time dataset for money, output, and prices. Overall, we find strong evidence against data rationality. Interestingly, for money stock the null is not rejected by linear tests but is rejected by our tests.

Keywords: bias, efficiency, generically comprehensive tests, rationality

JEL Classification: C32, C53, E01, E37, E47