Forecast Uncertainty and the Taylor Rule
36 Pages Posted: 21 Apr 2015 Last revised: 16 Jan 2017
Date Written: January 16, 2017
In this paper, we derive a modification of a forward-looking Taylor rule, which integrates two variables measuring the uncertainty of inflation and GDP growth forecasts into an otherwise standard New Keynesian model. We show that certainty-equivalence in New Keynesian models is a consequence of log-linearization and that a second-order Taylor approximation leads to a reaction function which includes the uncertainty of macroeconomic expectations. To test the model empirically, we use the standard deviation of individual forecasts around the median Consensus Forecast as proxy for forecast uncertainty. Our sample covers the euro area, Sweden, and the United Kingdom and the period 1992Q4-2014Q2. We find that while all three central banks react significantly to inflation forecast uncertainty by reducing their policy rates in times of higher inflation expectation uncertainty with an average effect of more than 25 basis points, they do not have significant reactions to GDP growth forecast uncertainty. We conclude with some implications for optimal monetary policy rules and central bank watchers.
Keywords: Certainty-Equivalence, Consensus Forecasts, Forecast Uncertainty, Global Financial Crisis, Optimal Monetary Policy, Taylor Rule
JEL Classification: E52, E58
Suggested Citation: Suggested Citation