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A state-dependent model for inflation forecasting

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Abstract
We develop a parsimonious bivariate model of inflation and unemployment that allows for persistent variation in trend inflation and the NAIRU. The model, which consists of five unobserved components (including the trends) with stochastic volatility, implies a time-varying VAR for changes in the rates of inflation and unemployment. The implied backwards-looking Phillips curve has a time-varying slope that is steeper in the 1970s than in the 1990s. Pseudo out-of-sample forecasting experiments indicate improvements upon univariate benchmarks. Since 2008, the implied Phillips curve has become steeper and the NAIRU has increased.

Suggested Citation

  • Andrea Stella & James H. Stock, 2012. "A state-dependent model for inflation forecasting," International Finance Discussion Papers 1062, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgif:1062
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    References listed on IDEAS

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    Cited by:

    1. Joshua C. C. Chan & Gary Koop & Simon M. Potter, 2016. "A Bounded Model of Time Variation in Trend Inflation, Nairu and the Phillips Curve," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 31(3), pages 551-565, April.
    2. Manuel Gonzalez-Astudillo & John M. Roberts, 2016. "When Can Trend-Cycle Decompositions Be Trusted?," Finance and Economics Discussion Series 2016-099, Board of Governors of the Federal Reserve System (U.S.).
    3. Murat Tasci & Randal J. Verbrugge, 2014. "How Much Slack Is in the Labor Market? That Depends on What You Mean by Slack," Economic Commentary, Federal Reserve Bank of Cleveland, issue Oct.
    4. Fabio Busetti & Pietro Cova & Antonio Maria Conti & Filippo Scoccianti & Libero Monteforte & Giordano Zevi & Valentina Aprigliano & Andrea Gerali & Alberto Locarno & Alessandro Notarpietro & Massimili, 2014. "The effects of the crisis on production potential and household spending in Italy," Workshop and Conferences 18, Bank of Italy, Economic Research and International Relations Area.
    5. Berger, Tino & Everaert, Gerdie & Vierke, Hauke, 2016. "Testing for time variation in an unobserved components model for the U.S. economy," Journal of Economic Dynamics and Control, Elsevier, vol. 69(C), pages 179-208.
    6. Florian Huber & Daniel Kaufmann, 2020. "Trend Fundamentals and Exchange Rate Dynamics," Economica, London School of Economics and Political Science, vol. 87(348), pages 1016-1036, October.
    7. Timothy Cogley & Thomas J. Sargent, 2014. "Measuring Price-Level Uncertainty and Instability in the U.S., 1850-2012," Working Papers 2014-33, Economic Research Institute, Bank of Korea.
    8. Szafranek, Karol, 2017. "Flattening of the New Keynesian Phillips curve: Evidence for an emerging, small open economy," Economic Modelling, Elsevier, vol. 63(C), pages 334-348.
    9. Sbrana, Giacomo & Silvestrini, Andrea & Venditti, Fabrizio, 2017. "Short-term inflation forecasting: The M.E.T.A. approach," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1065-1081.
    10. Nathan Sussman & Osnat Zohar, 2016. "Has Inflation Targeting Become Less Credible? Oil Prices, Global Aggregate Demand and Inflation Expectations during the Global Financial Crisis," Bank of Israel Working Papers 2016.13, Bank of Israel.
    11. Raïsa Basselier & David de Antonio Liedo & Jana Jonckheere & Geert Langenus, 2018. "Can inflation expectations in business or consumer surveys improve inflation forecasts?," Working Paper Research 348, National Bank of Belgium.
    12. Juan Carlos Berganza & Pedro del Río & Fructuoso Borrallo, 2016. "Determinants and implications of low global inflation rates," Occasional Papers 1608, Banco de España.
    13. Dany-Knedlik, Geraldine & Holtemöller, Oliver, 2017. "Inflation dynamics during the financial crisis in Europe: Cross-sectional identification of long-run inflation expectations," IWH Discussion Papers 10/2017, Halle Institute for Economic Research (IWH).
    14. Arnoud Stevens & Joris Wauters, 2021. "Is euro area lowflation here to stay? Insights from a time‐varying parameter model with survey data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 36(5), pages 566-586, August.
    15. Marianna Riggi & Fabrizio Venditti, 2014. "Surprise! Euro area inflation has fallen," Questioni di Economia e Finanza (Occasional Papers) 237, Bank of Italy, Economic Research and International Relations Area.
    16. Fabio Busetti & Michele Caivano & Lisa Rodano, 2015. "On the conditional distribution of euro area inflation forecast," Temi di discussione (Economic working papers) 1027, Bank of Italy, Economic Research and International Relations Area.
    17. Szafranek, Karol, 2019. "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1042-1059.
    18. Luis Uzeda, 2022. "State Correlation and Forecasting: A Bayesian Approach Using Unobserved Components Models," Advances in Econometrics, in: Essays in Honour of Fabio Canova, volume 44, pages 25-53, Emerald Group Publishing Limited.
    19. Fu, Bowen, 2020. "Is the slope of the Phillips curve time-varying? Evidence from unobserved components models," Economic Modelling, Elsevier, vol. 88(C), pages 320-340.
    20. Luis Julián Álvarez & Ana Gómez Loscos & Alberto Urtasun, 2015. "Asymmetries in the relationship between inflation and activity," Economic Bulletin, Banco de España, issue NOV, pages 3-9, November.
    21. Andrejs Bessonovs & Olegs Tkacevs, 2016. "Relationship Between Inflation and Economic Activity and Its Variation Over Time in Latvia," Working Papers 2016/03, Latvijas Banka.
    22. Joshua C.C. Chan & Todd E. Clark & Gary Koop, 2018. "A New Model of Inflation, Trend Inflation, and Long‐Run Inflation Expectations," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 50(1), pages 5-53, February.
    23. Michael O’Grady, 2019. "Estimating the Output, Inflation and Unemployment Gaps in Ireland using Bayesian Model Averaging," The Economic and Social Review, Economic and Social Studies, vol. 50(1), pages 35-76.

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