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

Listed author(s):
  • Kevin D Hoover
  • Selva Demiralp
  • Stephen J Perez

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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Paper provided by Duke University, Department of Economics in its series Working Papers with number 10-03.

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Length: 27
Date of creation: 2010
Handle: RePEc:duk:dukeec:10-03
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