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Forecasting Inflation: Phillips Curve Effects on Services Price Measures

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  • Ellis W. Tallman
  • Saeed Zaman

Abstract

We estimate an empirical model of inflation that exploits a Phillips curve relationship between a measure of unemployment and a subaggregate measure of inflation (services). We generate an aggregate inflation forecast from forecasts of the goods subcomponent separate from the services subcomponent, and compare the aggregated forecast to the leading time-series univariate and standard Phillips curve forecasting models. Our results indicate notable improvements in forecasting accuracy statistics for models that exploit relationships between services inflation and the unemployment rate. In addition, models of services inflation using the short-term unemployment rate (less than 27 weeks) as the real economic indicator display additional modest forecast accuracy improvements.

Suggested Citation

  • Ellis W. Tallman & Saeed Zaman, 2015. "Forecasting Inflation: Phillips Curve Effects on Services Price Measures," Working Papers (Old Series) 1519, Federal Reserve Bank of Cleveland.
  • Handle: RePEc:fip:fedcwp:1519
    DOI: 10.26509/frbc-wp-201519
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    Cited by:

    1. Marco Del Negro & Michele Lenza & Giorgio E. Primiceri & Andrea Tambalotti, 2020. "What's Up with the Phillips Curve?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(1 (Spring), pages 301-373.
    2. Tallman, Ellis W. & Zaman, Saeed, 2020. "Combining survey long-run forecasts and nowcasts with BVAR forecasts using relative entropy," International Journal of Forecasting, Elsevier, vol. 36(2), pages 373-398.
    3. Yunjong Eo & Luis Uzeda & Benjamin Wong, 2023. "Understanding trend inflation through the lens of the goods and services sectors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(5), pages 751-766, August.
    4. Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
    5. Saeed Zaman, 2019. "Cyclical versus Acyclical Inflation: A Deeper Dive," Economic Commentary, Federal Reserve Bank of Cleveland, issue September.
    6. Martins, Manuel Mota Freitas & Verona, Fabio, 2020. "Forecasting inflation with the New Keynesian Phillips curve: Frequency matters," Bank of Finland Research Discussion Papers 4/2020, Bank of Finland.
    7. Cobb, Marcus P A, 2018. "Improving Underlying Scenarios for Aggregate Forecasts: A Multi-level Combination Approach," MPRA Paper 88593, University Library of Munich, Germany.
    8. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
    9. James H. Stock & Mark W. Watson, 2019. "Slack and Cyclically Sensitive Inflation," NBER Working Papers 25987, National Bureau of Economic Research, Inc.
    10. Randal J. Verbrugge & Saeed Zaman, 2021. "Whose Inflation Expectations Best Predict Inflation?," Economic Commentary, Federal Reserve Bank of Cleveland, vol. 2021(19), pages 1-7, October.
    11. Keiichi Goshima & Hiroshi Ishijima & Mototsugu Shintani & Hiroki Yamamoto, 2019. "Forecasting Japanese inflation with a news-based leading indicator of economic activities," CARF F-Series CARF-F-458, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    12. Macias, Paweł & Stelmasiak, Damian & Szafranek, Karol, 2023. "Nowcasting food inflation with a massive amount of online prices," International Journal of Forecasting, Elsevier, vol. 39(2), pages 809-826.
    13. Randal Verbrugge & Saeed Zaman, 2024. "Post‐COVID inflation dynamics: Higher for longer," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(4), pages 871-893, July.
    14. Viacheslav Kramkov, 2023. "Does CPI disaggregation improve inflation forecast accuracy?," Bank of Russia Working Paper Series wps112, Bank of Russia.
    15. Verbrugge, Randal & Zaman, Saeed, 2024. "Improving inflation forecasts using robust measures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 735-745.
    16. Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig, 2021. "Forecasting the production side of GDP," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 458-480, April.
    17. Chalmovianský, Jakub & Porqueddu, Mario & Sokol, Andrej, 2020. "Weigh(t)ing the basket: aggregate and component-based inflation forecasts for the euro area," Working Paper Series 2501, European Central Bank.
    18. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    19. repec:fip:a00001:94156 is not listed on IDEAS
    20. repec:zbw:bofrdp:2020_004 is not listed on IDEAS
    21. James H. Stock & Mark W. Watson, 2020. "Slack and Cyclically Sensitive Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 52(S2), pages 393-428, December.
    22. Manuel M. F. Martins & Fabio Verona, 2020. "Forecasting Inflation with the New Keynesian Phillips Curve: Frequency Matters," CEF.UP Working Papers 2001, Universidade do Porto, Faculdade de Economia do Porto.
    23. Patrick C. Higgins, 2021. "The Phillips Curve during the Pandemic: Bringing Regional Data to Bear," Policy Hub, Federal Reserve Bank of Atlanta, vol. 2021(11), September.
    24. Randal J. Verbrugge, 2021. "Is It Time to Reassess the Focal Role of Core PCE Inflation?," Working Papers 21-10, Federal Reserve Bank of Cleveland.
    25. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.

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    More about this item

    Keywords

    Inflation forecasting; Phillips curve; disaggregated inflation forecasting models; trend-cycle model;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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