Research Database

The slope of the Phillips curve in New Keynesian models is difficult to estimate using aggregate data. We show that in a Bayesian estimation, the priors placed on the parameters governing nominal rigidities significantly influence posterior estimates and thus inferences about the importance of nominal rigidities. Conversely, we show that priors play a negligible role in a New Keynesian model estimated using state-level data. An estimation with state-level data exploits a relatively large panel dataset and removes the influence of endogenous monetary policy.

In this paper, we review the relationship between inflation rates, nominal interest rates, and rates of growth of monetary aggregates for a large group of OECD countries. We conclude that the low-frequency behavior of these series maintains a close relationship, as predicted by standard quantity theory models. In an estimated model, we show those relationships to be relatively invariant to alternative frictions that can deliver very different high-frequency dynamics. We argue that these relationships are useful for policy design aimed at controlling inflation.

The empirical literature on the stability of the Phillips curve has largely ignored the bias that endogenous monetary policy imparts on estimated Phillips curve coefficients. We argue that this omission has important implications. When policy is endogenous, estimation based on aggregate data can be uninformative as to the existence of a stable relationship between unemployment and future inflation. But we also argue that regional data can be used to identify the structural relationship between unemployment and inflation. Using city-level and state-level data from 1977 to 2017, we show that both the reduced form and the structural parameters of the Phillips curve are, to a substantial degree, quite stable over time.