Evaluating the New Keynesian Phillips Curve under VAR-Based Learning

This paper proposes the econometric evaluation of the New Keynesian Phillips Curve (NKPC) in the euro area, under a particular specification of the adaptive learning hypothesis. The key assumption is that agents? perceived law of motion is a Vector Autoregressive (VAR) model, whose coefficients are updated by maximum likelihood estimation, as the information set increases over time. Each time new data is available, likelihood ratio tests for the crossequation restrictions that the NKPC imposes on the VAR are computed and compared with a proper set of critical values which take the sequential nature of the test into account. The analysis is developed by focusing on the case where the variables entering the NKPC can be approximated as nonstationary cointegrated processes, assuming that the agents? recursive estimation algorithm involves only the parameters associated with the short run transient dynamics of the system. Results on quarterly data relative to the period 1981?2006 show that: (i) the euro area inflation rate and the wage share are cointegrated; (ii) the cointegrated version of the ?hybrid? NKPC is sharply rejected under the rational expectations hypothesis; (iii) the model is supported by the data over relevant fractions of the chosen monitoring period, 1986?2006, under the adaptive learning hypothesis, although this evidence does not appear compelling.