Models using service inflation better at predicting economic trends, Cleveland Fed paper finds
Tying unemployment rate with inflation in services can improve economic forecasting
Economic forecasting accuracy improves notably when based on models that exploit relationships between services inflation and the unemployment rate, according to a Federal Reserve Bank of Cleveland research paper.
Forecasting Inflation: Phillips Curve Effects on Services Price Measures, by Ellis Tallman and Saeed Zaman, estimates an empirical model of inflation that exploits a Phillips curve relationship between a measure of unemployment and a sub-aggregate measure of inflation.
Tallman and Zaman generate an "aggregate inflation forecast from forecasts of the goods sub-component separate from the services sub-component". They then compare the aggregated forecast to leading univariate and standard Phillips curve forecasting models.
The paper finds models of services inflation that use the unemployment rate of the past 27 weeks as the real economic indicator display additional modest forecast accuracy improvements.
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