Evaluating New Keynesian Phillips Curve under VAR-Based Learning

This paper proposes an 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 cross-equation restrictions that the NKPC imposes on the VAR coefficients are computed and compared with a proper set of critical values, which take the sequential nature of the test into account. The analysis focuses on the case in which the variables can be approximated as nonstationary cointegrated processes. Results on quarterly data relative to the period 1981 - 2006 show that: (i) the euro area inflation rate and the wage share are cointegrated, although their relationship does not appear stable during the eighties and first nineties; (ii) the cointegrated version of the 'hybrid' NKPC is sharply rejected under the rational expectations hypothesis; (iii) the NKPC is rejected also when the model is evaluated under a particular formulation of the adaptive learning hypothesis over the monitoring period 1986 - 2006.