Evaluating the New Keynesian Phillips Curve under VAR-based Learning

This paper proposes the evaluation of the New Keynesian Phillips Curve (NKPC) under a new learning mechanism where VAR learning dynamics is combined with the idea of testing the validity of the forward-looking model of inflation dynamics. The key assumption is that agents ’ perceived law of motion is a VAR whose parameters are updated by recursive least squares. Differently from standard adaptive learning methods, agents test sequentially the cross-equation restrictions that the NKPC imposes on the VAR as the information set increases. When the restrictions are not rejected agents learn under the restricted system and exploit the cross-equation restrictions to forecast inflation. It is thus possible to check how much and in which periods agents ’ beliefs are consistent with the restrictions of the theory. The empirical analysis on quarterly data on the euro area shows that the NKPC with negligible backward-looking parameter is not rejected when the model is evaluated over the period 1984-2005 under the proposed learning mechanism. The result, however, is not fully robust to specifications based on non stationary variables and points out that learning may represent a remarkable source of euro area inflation persistence but not its onl