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It can be shown that inflation expectations and associated forecast errors are characterized by a high degree of persistence. One reason may be that forecasters cannot directly observe the inflation target pursued by the central bank and, hence, face a complicated forecasting problem. In particular, they have to infer whether the observed movement of the inflation rate is due to a permanent change of policy parameters or whether it is the result of a transient shock. Consequently, it is assumed that agents behave like econometricians who filter noisy information by estimating an unobserved components model. This constitutes the trend learning algorithm employed by the forecaster. To examine whether this is a valid assumption, the authors fits a simple learning model to US survey expectations.
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