Understanding Post-Covid Inflation Dynamics
Journal of Monetary Economics,
We propose a macroeconomic model with a nonlinear Phillips curve that has a flat slope when inflationary pressures are subdued and steepens when inflationary pressures are elevated. The nonlinear Phillips curve in our model arises due to a quasi-kinked demand schedule for goods produced by firms. Our model can jointly account for the modest decline in inflation during the Great Recession and the surge in inflation during the post-COVID period. Because our model implies a stronger transmission of shocks when inflation is high, it generates conditional heteroskedasticity in inflation and inflation risk. Hence, our model can generate more sizeable inflation surges due to cost-push and demand shocks than a standard linearized model. Finally, our model implies that the central bank faces a more severe trade-off between inflation and output stabilization when inflation is elevated.
Evaluating the German (New Keynesian) Phillips Curve
IWH Discussion Papers,
This paper evaluates the New Keynesian Phillips Curve (NKPC) and its hybrid
variant within a limited information framework for Germany. The main interest rests on the average frequency of price re-optimization of ﬁrms. We use the labor income share as the driving variable and consider a source of real rigidity by allowing for a ﬁxed ﬁrm-speciﬁc capital stock. A GMM estimation strategy is employed as well as an identiﬁcation robust method that is based upon the Anderson-Rubin statistic. We ﬁnd out that the German Phillips Curve is purely forward looking. Moreover, our point estimates are consistent with the view that ﬁrms re-optimize prices every two to three quarters. While these estimates seem plausible from an economic point of view, the uncertainties around these estimates are very large and also consistent with perfect nominal price rigidity where ﬁrms never re-optimize prices. This analysis also oﬀers some explanations why previous results for the German NKPC based on GMM diﬀer considerably. First, standard GMM results are very sensitive to the way how orthogonality conditions are formulated. Additionally, model misspeciﬁcations may be left undetected by conventional J tests. Taken together, this analysis points out
the need for identiﬁcation robust methods to get reliable estimates for the NKPC.