Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2

Abstract

The M2 monetary aggregate is monitored by the Federal Reserve, using a broad brush theoretical analysis and an informal empirical analysis. This paper illustrates empirical identification of an eleven-variable system, in which M2 and the factors that the Fed regards as causes and effects are captured in a vector autogregression. Taking account of cointegration, the methodology combines recent developments in graph-theoretical causal search algorithms with a general-to-specific search algorithm to identify a fully specified structural vector autoregression (SVAR). The SVAR is used to examine the causes and effects of M2 in a variety of ways. We conclude that, while the Fed has rightly identified a number of special factors that influence M2 and while M2 detectably affects other important variables, there is 1) little support for the core quantity-theoretic approach to M2 used by the Fed; and 2) M2 is a trivial linkage in the transmission mechanism from monetary policy to real output and inflation.