2016•Federal Reserve Bank of Dallas, Working PapersOpen access

The Roles of Inflation Expectations, Core Inflation, and Slack in Real-Time Inflation Forecasting

Nand Kishor, EVAN F. KOENIG, Federal Reserve Bank of Dallas

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Abstract

Using state-space modeling, we extract information from surveys of long-term in- ‡ation expectations and multiple quarterly in ‡ation series to undertake a real-time decomposition of quarterly headline PCE and GDP-de ‡ator in ‡ation rates into a common long-term trend, common cyclical component, and high-frequency noise components.We then explore alternative approaches to real-time forecasting of headline PCE in ‡ation.We …nd that performance is enhanced if forecasting equations are estimated using in ‡ation data that have been stripped of high-frequency noise.Performance can be further improved by including an unemployment-based measure of slack in the equations.The improvement is statistically signi…cant relative to benchmark autoregressive models and also relative to professional forecasters at all but the shortest horizons.In contrast, introducing slack into models estimated using headline PCE in ‡ation data or conventional core in ‡ation data causes forecast performance to deteriorate.Finally, we demonstrate that forecasting models estimated using the Kishor-Koenig (2012) methodology-which mandates that each forecasting VAR be augmented with a ‡exible state-space model of data revisions-consistently outperform the corresponding conventionally estimated forecasting models.

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Using state-space modeling, we extract information from surveys of long-term in- ‡ation expectations and multiple quarterly in ‡ation series to undertake a real-time decomposition of quarterly headline PCE and GDP-de ‡ator in ‡ation rates into a common long-term trend, common cyclical component, and high-frequency noise components.We then explore alternative approaches to real-time forecasting of headline PCE in ‡ation.We …nd that performance is enhanced if forecasting equations are estimated using in ‡ation data that have been stripped of high-frequency noise.Performance can be further improved by including an unemployment-based measure of slack in the equations.The improvement is statistically signi…cant relative to benchmark autoregressive models and also relative to professional forecasters at all but the shortest horizons.In contrast, introducing slack into models estimated using headline PCE in ‡ation data or conventional core in ‡ation data causes forecast performance to deteriorate.Finally, we demonstrate that forecasting models estimated using the Kishor-Koenig (2012) methodology-which mandates that each forecasting VAR be augmented with a ‡exible state-space model of data revisions-consistently outperform the corresponding conventionally estimated forecasting models.

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Available abstract

Using state-space modeling, we extract information from surveys of long-term in- ‡ation expectations and multiple quarterly in ‡ation series to undertake a real-time decomposition of quarterly headline PCE and GDP-de ‡ator in ‡ation rates into a common long-term trend, common cyclical component, and high-frequency noise components.We then explore alternative approaches to real-time forecasting of headline PCE in ‡ation.We …nd that performance is enhanced if forecasting equations are estimated using in ‡ation data that have been stripped of high-frequency noise.Performance can be further improved by including an unemployment-based measure of slack in the equations.The improvement is statistically signi…cant relative to benchmark autoregressive models and also relative to professional forecasters at all but the shortest horizons.In contrast, introducing slack into models estimated using headline PCE in ‡ation data or conventional core in ‡ation data causes forecast performance to deteriorate.Finally, we demonstrate that forecasting models estimated using the Kishor-Koenig (2012) methodology-which mandates that each forecasting VAR be augmented with a ‡exible state-space model of data revisions-consistently outperform the corresponding conventionally estimated forecasting models.

Key concepts: Core inflation, Inflation (cosmology), Econometrics, Headline, Autoregressive model, Nowcasting, Benchmark (surveying), Economics

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