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Forecasting Core Inflation and Its Goods, Housing, and Supercore Components

Todd Clark, Matthew Gordon and Saeed Zaman

No 23-34, Working Papers from Federal Reserve Bank of Cleveland

Abstract: This paper examines the forecasting efficacy and implications of the recently popular breakdown of core inflation into three components: goods excluding food and energy, services excluding energy and housing, and housing. A comprehensive historical evaluation of the accuracy of point and density forecasts from a range of models and approaches shows that a BVAR with stochastic volatility in aggregate core inflation, its three components, and wage growth is an effective tool for forecasting inflation's components as well as aggregate core inflation. Looking ahead, the model's baseline projection puts core inflation at 2.6 percent in 2026, well below its 2023 level but still elevated relative to the Federal Reserve's 2 percent objective. The probability that core inflation will return to 2 percent or less is much higher when conditioning on goods or non-housing services inflation slowing to pre-pandemic levels than when conditioning on these components remaining above the same thresholds. Scenario analysis indicates that slower wage growth will likely be associated with reduced inflation in all three components, especially goods and non-housing services, helping to return core inflation to near the 2 percent target by 2026.

Keywords: Supercore inflation; forecast aggregation; Bayesian vector autoregression; scenario analysis (search for similar items in EconPapers)
JEL-codes: C32 C53 E17 E31 E37 (search for similar items in EconPapers)
Pages: 45
Date: 2023-12-20
New Economics Papers: this item is included in nep-for and nep-mon
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DOI: 10.26509/frbc-wp-202334

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