Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
A simulation study is designed to evaluate the sensitivity of inference in a Vector Autoregression in which the variables of interest (GNP, the money stock, the price level, and a short-term interest rate) are treated as trend stationary processes. Using the normal asymptotic theory, the authors find that an artificially generated random walk Granger-causes the genuine variables in the model as often as 60% of the time for a 5% level test. They also observe substantial bias when other persistent stochastic processes are included in the autoregressions. The number of rejections are two to five times greater than if the variables are not linearly detrended prior to analysis. Copyright 1991 by MIT Press.