Permanent and Transitory Policy Shocks in an Empirical Macro Model with Asymmetric Information



the binary dummy, the inflation target was gradually changed by η in each quarter of the
1979Q4 through 1982Q4 interval. A competing description is that the target was completely
adjusted in one quarter, 1979Q4, but this provides a poor estimate of
η. Increasing the
period over which the target change occurred reduces the standard error of the estimate
of
η. The 1979Q4 to 1982Q4 interval is also a period when U.S. monetary policy targeted
measures of the money supply and nonborrowed reserves, and there were several adjustments
of targets and operating procedures.

Exogenous policy shocks may proxy for changes in the degree of conservatism of the
Federal Open Market Committee (FOMC), at least to the extent that this accompanies a
change in the composition of the FOMC, and importantly, the Chairman. Other possible
explanations for target changes include partial accommodation of changing expectations of
the private sector, as in Bullard and Cho (2002), or partial accommodation of movements
in inflation, as in GUrkaynak, Sack, and Swanson (2003). The former is inconsistent with
the target leading the perceived target, as was likely the case in the Volker disinflation.
The latter might be seen as a reduced-form variant of partial accommodation of aggregate
supply shocks as specified in this paper.

The evolution of the perceived inflation target incorporates learning behavior about
the target on the part of the private sector. Several approaches to modeling learning
behavior that could be used to close the model include recursive least squares learning,
Markov switching, changepoint learning, and constant gain learning. In models with a fixed
structure, recursive least squares estimates of parameters can converge to the true parameter
values. However, recursive least squares loses its appeal in models with structural change
as distant past observations are weighted equally to recent observations. Markov switching
models are effective in specifications where there is advance knowledge of the number of
regimes. However, empirical implementations do not accommodate regimes that have not
yet occurred (with no data points in a regime, it is impossible to estimate the parameters

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