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Improving the Phillips Curve with an Interaction Variable

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  • Kevin J. Lansing

Abstract

A key challenge for monetary policymakers is to predict where inflation is headed. One promising approach involves modifying a typical Phillips curve predictive regression to include an interaction variable, defined as the multiplicative combination of lagged inflation and the lagged output gap. This variable appears better able to capture the true underlying inflationary pressure associated with the output gap itself. Including the interaction variable helps improve the accuracy of Phillips curve inflation forecasts over various sample periods.

Suggested Citation

  • Kevin J. Lansing, 2019. "Improving the Phillips Curve with an Interaction Variable," FRBSF Economic Letter, Federal Reserve Bank of San Francisco.
    • Handle: RePEc:fip:fedfel:00193
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    References listed on IDEAS

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    1. Olivier Coibion & Yuriy Gorodnichenko, 2015. "Is the Phillips Curve Alive and Well after All? Inflation Expectations and the Missing Disinflation," American Economic Journal: Macroeconomics, American Economic Association, vol. 7(1), pages 197-232, January.
    2. Kevin J. Lansing, 2002. "Can the Phillips curve help forecast inflation?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue oct4.
    3. Kevin J. Lansing, 2006. "Will moderating growth reduce inflation?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue dec22.
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    Cited by:

    1. Augustus J. Panton, 2020. "Climate hysteresis and monetary policy," CAMA Working Papers 2020-76, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    2. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.

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