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Using a Wage–Price‐Setting Model to Forecast US Inflation

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  • Nguyen Duc Do

Abstract

This study modifies a wage–price‐setting (WPS) model to forecast US inflation over 1‐ to 3‐year horizons, based on the assumption that firms use a rule of thumb to set prices after settling a wage agreement. The out‐of‐sample forecast results show that productivity growth is a powerful predictor of inflation, in the sense that during the 1990Q1–2023Q4 period, the modified WPS model improved upon some univariate benchmark models and multivariate models such as the Phillips curve, term spread, and wage‐inflation models. From the early 2000s to the prepandemic period, forecast accuracy was improved by combining productivity growth with anchored inflation expectations. Interestingly, during this period, forecasts derived from the WPS model with constant‐inflation expectations were found to slightly outperform Greenbook forecasts in forecasting quarter‐over‐quarter inflation from two‐ to four‐quarter horizons.

Suggested Citation

  • Nguyen Duc Do, 2025. "Using a Wage–Price‐Setting Model to Forecast US Inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(2), pages 803-832, March.
    • Handle: RePEc:wly:jforec:v:44:y:2025:i:2:p:803-832
    DOI: 10.1002/for.3210
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