Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2
| dc.contributor.author | Hoover, KD | |
| dc.contributor.author | Demiralp, S | |
| dc.contributor.author | Perez, SJ | |
| dc.date.accessioned | 2010-03-09T15:42:08Z | |
| dc.date.available | 2010-03-09T15:42:08Z | |
| dc.date.issued | 2008 | |
| dc.description.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. | |
| dc.format.extent | 384513 bytes | |
| dc.format.mimetype | application/pdf | |
| dc.identifier.uri | ||
| dc.language.iso | en_US | |
| dc.publisher | Oxford University Press | |
| dc.title | Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2 | |
| dc.type | Journal article |
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