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Time-Varying U.S. Inflation Dynamics and the New Keynesian Phillips Curve

  • Kevin J. Lansing

    (Federal Reserve Bank of San Francisco)

This paper introduces a form of boundedly-rational expectations into an otherwise standard New Keynesian Phillips curve. The representative agent's forecast rule is optimal, conditional on a perceived law of motion for inflation and observed moments of the inflation time series. The perceived law of motion allows for both temporary and permanent shocks, the latter intended to capture the possibility of evolving shifts in the central bank's inflation target. In this case, the agent's optimal forecast rule defined by the Kalman filter coincides with adaptive expectations, as shown originally by Muth (1960). I show that the perceived optimal value of the Kalman gain parameter is given by the fixed point of a nonlinear map that relates the gain to the autocorrelation of inflation changes. The model allows for either a constant gain or variable gain, depending on the length of the sample period used by the agent to compute the autocorrelation of inflation changes. In the variable-gain setup, the law of motion for inflation is nonlinear and can generate time-varying inflation dynamics similar to those in long-run U.S. data. The model's inflation dynamics are driven solely by white-noise fundamental shocks propagated via the expectations feedback mechanism; all monetary policy-dependent parameters are held constant

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Paper provided by Society for Computational Economics in its series Computing in Economics and Finance 2006 with number 488.

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Date of creation: 04 Jul 2006
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Handle: RePEc:sce:scecfa:488
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