U.S. Wage and Price Dynamics: A Limited-Information Approach

This paper analyzes the dynamics of prices and wages using a limited-information approach to estimation. I estimate a two-equation model for the determination of prices and wages derived from an optimization-based dynamic model, where both goods and labor markets are monopolistically competitive, prices and wages can be reoptimized only at random intervals, and, when not reoptimized, can be partially adjusted to previous-period aggregate inflation. The estimation procedure is a two-step minimum-distance estimation, which exploits the restrictions that the model imposes on a time-series representation of the data. In the first step I estimate an unrestricted autoregressive representation of the variables of interest. In the second step, I express the model solution in the form of a constrained autoregressive representation of the data and define the distance between unconstrained and constrained representations as a function of the structural parameters that characterize the joint dynamics of inflation and labor share. This function summarizes the cross-equation restrictions between the model and the time-series representations of the data: I then estimate the parameters of interest by minimizing a quadratic function of that distance. I find that the estimated dynamics of prices and wages track actual dynamics quite well, and that the estimated parameters are consistent with the observed length of nominal contracts.