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.
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https://hdl.handle.net/10161/2035Collections
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Kevin Douglas Hoover
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

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