Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2
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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.
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Professor of Economics
Professor Hoover's research interests include macroeconomics, monetary economics, the history of economics, and the philosophy and methodology of empirical economics. His recent work in economics has focused on the application of causal search methodologies for structural vector autoregression, the history of microfoundational programs in macroeconomics, and Roy Harrod's early work on dynamic macroeconomics. In philosophy, he has concentrated on issues related to causality, especially in economi