IDEAS home Printed from https://ideas.repec.org/p/fip/fedcwq/95478.html
   My bibliography  Save this paper

Post-COVID Inflation Dynamics: Higher for Longer

Author

Listed:
  • Randal J. Verbrugge
  • Saeed Zaman

Abstract

We implement a novel nonlinear structural model featuring an empirically-successful frequency-dependent and asymmetric Phillips curve; unemployment frequency components interact with three components of core PCE – core goods, housing, and core services ex-housing – and a variable capturing supply shocks. Forecast tests verify model’s accuracy in its unemployment-inflation tradeoffs, crucial for monetary policy. Using this model, we assess the plausibility of the December 2022 Summary of Economic Projections (SEP). By 2025Q4, the SEP projects 2.1 percent inflation; however, conditional on the SEP unemployment path, we project inflation of 2.9 percent. A fairly deep recession delivers the SEP inflation path, but a simple welfare analysis rejects this outcome.

Suggested Citation

  • Randal J. Verbrugge & Saeed Zaman, 2023. "Post-COVID Inflation Dynamics: Higher for Longer," Working Papers 23-06R, Federal Reserve Bank of Cleveland, revised 20 Jun 2023.
  • Handle: RePEc:fip:fedcwq:95478
    DOI: 10.26509/frbc-wp-202306r
    as

    Download full text from publisher

    File URL: https://doi.org/10.26509/frbc-wp-202306r
    File Function: Persistent Link
    Download Restriction: no

    File URL: https://www.clevelandfed.org/-/media/project/clevelandfedtenant/clevelandfedsite/publications/working-papers/2023/wp2306r.pdf
    File Function: Full Text
    Download Restriction: no

    File URL: https://libkey.io/10.26509/frbc-wp-202306r?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Alessio Moneta, 2008. "Graphical causal models and VARs: an empirical assessment of the real business cycles hypothesis," Empirical Economics, Springer, vol. 35(2), pages 275-300, September.
    2. Richard A. Ashley & Randal J. Verbrugge, 2009. "To difference or not to difference: a Monte Carlo investigation of inference in vector autoregression models," International Journal of Data Analysis Techniques and Strategies, Inderscience Enterprises Ltd, vol. 1(3), pages 242-274.
    3. Todd E. Clark & Saeed Zaman, 2013. "Forecasting implications of the recent decline in inflation," Economic Commentary, Federal Reserve Bank of Cleveland, issue Nov.
    4. Richard Ashley & Kwok Ping Tsang & Randal Verbrugge, 2020. "A new look at historical monetary policy (and the great inflation) through the lens of a persistence-dependent policy rule," Oxford Economic Papers, Oxford University Press, vol. 72(3), pages 672-691.
    5. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October.
    6. Mise, Emi & Kim, Tae-Hwan & Newbold, Paul, 2005. "On suboptimality of the Hodrick-Prescott filter at time series endpoints," Journal of Macroeconomics, Elsevier, vol. 27(1), pages 53-67, March.
    7. Randal Verbrugge, 2022. "Is it Time to Reassess the Focal Role of Core PCE Inflation in Assessing the Trend in PCE Inflation?," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 73-101.
    8. Andrew Figura & Christopher J. Waller, 2022. "What does the Beveridge curve tell us about the likelihood of a soft landing?," FEDS Notes 2022-07-29, Board of Governors of the Federal Reserve System (U.S.).
    9. George-Marios Angeletos & Fabrice Collard & Harris Dellas, 2020. "Business-Cycle Anatomy," American Economic Review, American Economic Association, vol. 110(10), pages 3030-3070, October.
    10. Selva Demiralp & Kevin D. Hoover & Stephen J. Perez, 2008. "A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 70(4), pages 509-533, August.
    11. Paul Beaudry & Dana Galizia & Franck Portier, 2020. "Putting the Cycle Back into Business Cycle Analysis," American Economic Review, American Economic Association, vol. 110(1), pages 1-47, January.
    12. Hall, Robert E. & Kudlyak, Marianna, 2022. "The unemployed with jobs and without jobs," Labour Economics, Elsevier, vol. 79(C).
    13. Alessandra Iacobucci & Alain Noullez, 2005. "A Frequency Selective Filter for Short-Length Time Series," Computational Economics, Springer;Society for Computational Economics, vol. 25(1), pages 75-102, February.
    14. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).
    15. Regina Kaiser & Agustín Maravall, 1999. "Estimation of the business cycle: A modified Hodrick-Prescott filter," Spanish Economic Review, Springer;Spanish Economic Association, vol. 1(2), pages 175-206.
    16. Gallin, Joshua & Verbrugge, Randal J., 2019. "A theory of sticky rents: Search and bargaining with incomplete information," Journal of Economic Theory, Elsevier, vol. 183(C), pages 478-519.
    17. Clark, Todd E. & Kozicki, Sharon, 2005. "Estimating equilibrium real interest rates in real time," The North American Journal of Economics and Finance, Elsevier, vol. 16(3), pages 395-413, December.
    18. Glymour, Clark & Spirtes, Peter, 1988. "Latent variables, causal models and overidentifying constraints," Journal of Econometrics, Elsevier, vol. 39(1-2), pages 175-198.
    19. Phillips, Kerk L. & Spencer, David E., 2011. "Bootstrapping structural VARs: Avoiding a potential bias in confidence intervals for impulse response functions," Journal of Macroeconomics, Elsevier, vol. 33(4), pages 582-594.
    20. Lawrence J. Christiano & Terry J. Fitzgerald, 2003. "The Band Pass Filter," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 44(2), pages 435-465, May.
    21. James Morley & Jeremy Piger, 2012. "The Asymmetric Business Cycle," The Review of Economics and Statistics, MIT Press, vol. 94(1), pages 208-221, February.
    22. 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.
    23. James H. Stock & Mark W. Watson, 2010. "Modeling Inflation After the Crisis," Working Papers 2010-1, Princeton University. Economics Department..
    24. Aguiar-Conraria, Luís & Martins, Manuel M.F. & Soares, Maria Joana, 2023. "The Phillips curve at 65: Time for time and frequency," Journal of Economic Dynamics and Control, Elsevier, vol. 151(C).
    25. Kristin J. Forbes & Joseph E. Gagnon & Christopher G. Collins, 2022. "Low Inflation Bends the Phillips Curve around the World," Revista Economía, Fondo Editorial - Pontificia Universidad Católica del Perú, vol. 45(89), pages 52-72.
    26. Potter, Simon M., 2000. "Nonlinear impulse response functions," Journal of Economic Dynamics and Control, Elsevier, vol. 24(10), pages 1425-1446, September.
    27. Lutz Kilian & Robert J. Vigfusson, 2011. "Are the responses of the U.S. economy asymmetric in energy price increases and decreases?," Quantitative Economics, Econometric Society, vol. 2(3), pages 419-453, November.
    28. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    29. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, November.
    30. James D. Hamilton, 2018. "Why You Should Never Use the Hodrick-Prescott Filter," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 831-843, December.
    31. Selva Demiralp & Kevin D. Hoover, 2003. "Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 65(s1), pages 745-767, December.
    32. James H. Stock & Mark W. Watson, 2010. "Modeling inflation after the crisis," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 173-220.
    33. Tallman, Ellis W. & Zaman, Saeed, 2017. "Forecasting inflation: Phillips curve effects on services price measures," International Journal of Forecasting, Elsevier, vol. 33(2), pages 442-457.
    34. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    35. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Verbrugge, Randal & Zaman, Saeed, 2024. "Improving inflation forecasts using robust measures," International Journal of Forecasting, Elsevier, vol. 40(2), pages 735-745.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Verbrugge, Randal & Zaman, Saeed, 2023. "The hard road to a soft landing: Evidence from a (modestly) nonlinear structural model," Energy Economics, Elsevier, vol. 123(C).
    2. Josefine Quast & Maik H. Wolters, 2022. "Reliable Real-Time Output Gap Estimates Based on a Modified Hamilton Filter," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(1), pages 152-168, January.
    3. Bańbura, Marta & Bobeica, Elena, 2023. "Does the Phillips curve help to forecast euro area inflation?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 364-390.
    4. Saeed Zaman, 2021. "A Unified Framework to Estimate Macroeconomic Stars," Working Papers 21-23R2, Federal Reserve Bank of Cleveland, revised 31 May 2024.
    5. Bettendorf, Timo & Heinlein, Reinhold, 2019. "Connectedness between G10 currencies: Searching for the causal structure," Discussion Papers 06/2019, Deutsche Bundesbank.
    6. Cordoni, Francesco & Dorémus, Nicolas & Moneta, Alessio, 2024. "Identification of vector autoregressive models with nonlinear contemporaneous structure," Journal of Economic Dynamics and Control, Elsevier, vol. 162(C).
    7. Wang, Zijun, 2012. "The causal structure of bond yields," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(1), pages 93-102.
    8. Piyachart Phiromswad & Takeshi Yagihashi, 2016. "Empirical identification of factor models," Empirical Economics, Springer, vol. 51(2), pages 621-658, September.
    9. Viv B. Hall & Peter Thomson, 2021. "Does Hamilton’s OLS Regression Provide a “better alternative” to the Hodrick-Prescott Filter? A New Zealand Business Cycle Perspective," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 17(2), pages 151-183, November.
    10. Richard A. Ashley & Randall J. Verbrugge., 2006. "Mis-Specification in Phillips Curve Regressions: Quantifying Frequency Dependence in This Relationship While Allowing for Feedback," Working Papers e06-11, Virginia Polytechnic Institute and State University, Department of Economics.
    11. Bruns, Stephan B. & Moneta, Alessio & Stern, David I., 2021. "Estimating the economy-wide rebound effect using empirically identified structural vector autoregressions," Energy Economics, Elsevier, vol. 97(C).
    12. Andrew Rettenmaier & Zijun Wang, 2013. "What determines health: a causal analysis using county level data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(5), pages 821-834, October.
    13. Alessio Moneta & Doris Entner & Patrik O. Hoyer & Alex Coad, 2013. "Causal Inference by Independent Component Analysis: Theory and Applications," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(5), pages 705-730, October.
    14. Henry L. Bryant & David A. Bessler & Michael S. Haigh, 2009. "Disproving Causal Relationships Using Observational Data," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 357-374, June.
    15. Timo Bettendorf & Reinhold Heinlein, 2023. "Connectedness between G10 currencies: Searching for the causal structure," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(4), pages 3938-3959, October.
    16. Piyachart Phiromswad, 2014. "Measuring monetary policy with empirically grounded identifying restrictions," Empirical Economics, Springer, vol. 46(2), pages 681-699, March.
    17. Kevin D. Hoover, 2020. "The Discovery of Long-Run Causal Order: A Preliminary Investigation," Econometrics, MDPI, vol. 8(3), pages 1-25, August.
    18. Wongboonsin, Kua & Phiromswad, Piyachart, 2017. "Searching for empirical linkages between demographic structure and economic growth," Economic Modelling, Elsevier, vol. 60(C), pages 364-379.
    19. Ahelegbey, Daniel Felix, 2015. "The Econometrics of Bayesian Graphical Models: A Review With Financial Application," MPRA Paper 92634, University Library of Munich, Germany, revised 25 Apr 2016.
    20. Richard A. Ashley. & Randall J. Verbrugge., 2006. "Mis-Specification and Frequency Dependence in a New Keynesian Phillips Curve," Working Papers e06-12, Virginia Polytechnic Institute and State University, Department of Economics.

    More about this item

    Keywords

    Nonlinear Phillips Curve; Frequency Decomposition; Supply Price Pressures; Structural VAR; Nonlinear Impulse Response Functions; Welfare Analysis;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:fip:fedcwq:95478. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: 4D Library (email available below). General contact details of provider: https://edirc.repec.org/data/frbclus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.