U.S. Wage‐Price Dynamics, Before, During and After COVID‐19, Through the Lens of an Empirical Econometric Model
构建了一个包含均衡修正项的多方程模型，连接通胀与工资份额及功能性收入分配，估计了美国1960年代以来的季度数据，发现工资增长在疫情初期重要，但后期国际价格上升等因素更关键，且模型能较好预测2023年初以来的通胀。
ABSTRACT 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.