Empirical Identification of the Vector Autoregression: The Causes and Effects of US M2

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© Jennifer L. Castle & Neil Shephard 2009. All rights reserved. The M2 monetary aggregate is monitored by the Federal Reserve, using a broad brush theoretical analysis and an informal empirical analysis. This chapter 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 autoregression. 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. The chapter concludes 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.

Full Text

Duke Authors

Cited Authors

  • Hoover, KD; Demiralp, S; Perez, SJ

Published Date

  • September 1, 2009

Book Title

  • The Methodology and Practice of Econometrics: A Festschrift in Honour of David F. Hendry

International Standard Book Number 13 (ISBN-13)

  • 9780199237197

Digital Object Identifier (DOI)

  • 10.1093/acprof:oso/9780199237197.003.0002

Citation Source

  • Scopus