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...

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...

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...

We evaluate and compare alternative monetary policy rules, namely average inflation targeting, price level targeting, and traditional inflation targeting rules, in a standard New Keynesian model that features recurring, transient zero lower bound regimes. We use determinacy and expectational...

This paper studies price stability and debt sustainability when the real rate exceeds trend growth (r g) in a New Keynesian model with endogenous technology growth through R&D. Endogenous growth constitutes a self-financing mechanism for deficits which backs debt and attenuates fiscal...

This paper studies price stability and debt sustainability when the real rate exceeds trend growth (r g) in a New Keynesian model with endogenous technology growth through R&D. Under debt-stabilizing ("passive") fiscal policy the Taylor principle is not sufficient for determinacy. Instead,...

We show that sacrifice ratios associated with announcements of the most likely course of monetary policy are lower when the implementation date is further out into the future in the basic New Keynesian framework. This is not due to forward guidance puzzle effects and holds even when agents’...