U.S. Wage‐Price Dynamics, Before, During and After COVID‐19, Through the Lens of an Empirical Econometric Model

We specify a multiple‐equation model with equilibrium‐correction terms, which connect inflation to the wage share and the functional income distribution, while not excluding a priori variables that are typically found in existing empirical U.S. Phillips curve models. We estimate the model equations using automatic variable selection with low Type‐1 error probabilities on a sample with quarterly data that starts in the 1960s. Conditional on a relatively small number of location shift indicators, the price and wage equations have relatively constant parameters. The model's explanatory power is shown by dynamic simulations. Applied to the COVID‐19 period, the model shows that wage growth was important initially but that other factors later also became important, in particular the broad increase in international prices. Out‐of‐sample simulation shows how well the model forecasts inflation since early 2023.