This paper considers the performance of average inflation targeting (AIT) policy in a New Keynesian model with adaptive learning agents. Our analysis raises concerns regarding robustness of AIT when agents have imperfect knowledge. In particular, the target steady state can be locally unstable...

This paper considers the performance of average inflation targeting (AIT) policy in a New Keynesian model with adaptive learning agents. Our analysis raises concerns regarding robustness of AIT when agents have imperfect knowledge. In particular, the target steady state can be locally unstable...

This paper considers the performance of average inflation targeting (AIT) policy when there is imperfect knowledge. There are concerns regarding robustness of AIT in a New Keynesian model with learning agents. The target steady state may not be robustly stable under adaptive learning if the...

Properties of average inflation targeting under imperfect knowledge and learning have been studied only for the Rotemberg NK model, where price stickiness arises from adjustment costs in price setting. This note fills the gap by studying average inflation targeting in the NK model with Calvo...

Standard New Keynesian models predict implausibly large and favorable responses of inflation and output to expansionary forward guidance on interest rates. We find that the introduction of permanent or recurring active fiscal policy dampens the response of output and inflation to forward...

This paper examines the uniqueness and learnability of rational expectations equilibrium when the policy rate is occasionally pegged at the zero lower bound (ZLB). We consider a model that features recurring, transient ZLB regimes and compare various interest rate rules which respond to...

New Keynesian models generate puzzles when confronted with the zero lower bound (ZLB) on nominal interest rates (e.g. the forward guidance puzzle or the paradox of flexibility). We show that these puzzles are absent in simple and medium-scale models when monetary policy approximates optimal...