that characterize the regime).
Kozicki and Tinsley (2001a and 2001b) use changepoint learning in a model with possibly
multiple regimes when the number of regimes is not predetermined. Learning about target
shifts is modeled by assuming agents use Andrews (1993) breakpoint tests to detect mean
shifts in inflation. Learning lags are captured by dating expectations shifts according to
the time at which agents could statistically detect mean shifts, which necessarily lags the
actual occurrence of mean shifts. Heterogeneity of agents is captured using alternative
implementations of the test based on the number of new observations collected prior to
retesting. Kozicki and Tinsley (2001a) use multinominal logit aggregation of heterogeneous
learning that allows weights on different breakpoint-test implementations to depend on
recent performance. An advantage of changepoint learning is that it will converge in the
absence of structural shifts. A disadvantage is that testing for change points is not amenable
to recursive formulations.
Constant gain learning is similar to recursive least squares but gives more weight
to more recent observations to capture effects of structural shifts. However, in the
absence of shifts, constant gain learning will generate noisy estimates of parameters
(natural rates) as disturbances to observable data series will be mapped into perceptions.
Although changepoint learning and constant gain learning have very different properties,
Kozicki and Tinsley (2001b) found that inflation-based constant gain learning in which
the perceived target is updated in the direction of recent inflation (πP (t +1) = πP (t)+
κ(πt - πP (t))) approximated the estimated perceived inflation target constructed using
changepoint-learning with heterogeneous agents.
This paper uses constant gain updating rules to model learning about structural shifts
in the inflation target. The benefits of this approach include the potential for capturing
permanent structural shifts, the adaptability to recursive formulations, and the absence of
sharp prior requirements, such as advance knowledge of the number of policy regimes. The
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