The St. Louis Equation and Reverse Causation: The Evidence Reexamined

Abstract

A major criticism of the so-called St. Louis equation, Andersen and Jordan [2], has been that it is not a true reduced form equation since the monetary policy variable is not statistically exogenous with respect to GNP [4; 6; 7; 9], and therefore, it is inappropriate to estimate by ordinary least squares the distributed lag regression of nominal income on monetary and fiscal policy variables. Quite recently, however, statistical techniques1 have been developed to test whether or not the explanatory variables in a regression equation can be regarded as strictly exogenous [16; 17; 18; 15]. Of great interest is the work by Sims [16] who has shown that in a distributed lag regression of nominal income on monetary policy variables, monetary policy variables such as the monetary base can be regarded as strictly exogenous with respect to nominal GNP.