IDEAS home Printed from https://ideas.repec.org/p/diw/diwwpp/dp1285.html
   My bibliography  Save this paper

A Noncausal Autoregressive Model with Time-Varying Parameters: An Application to U.S. Inflation

Author

Listed:
  • Markku Lanne
  • Jani Luoto

Abstract

We propose a noncausal autoregressive model with time-varying parameters, and apply it to U.S. postwar inflation. The model .fits the data well, and the results suggest that inflation persistence follows from future expectations. Persistence has declined in the early 1980.s and slightly increased again in the late 1990.s. Estimates of the new Keynesian Phillips curve indicate that current inflation also depends on past inflation although future expectations dominate. The implied trend inflation estimate evolves smoothly and is well aligned with survey expectations. There is evidence in favor of the variation of trend inflation following from the underlying marginal cost that drives inflation.

Suggested Citation

  • Markku Lanne & Jani Luoto, 2013. "A Noncausal Autoregressive Model with Time-Varying Parameters: An Application to U.S. Inflation," Discussion Papers of DIW Berlin 1285, DIW Berlin, German Institute for Economic Research.
  • Handle: RePEc:diw:diwwpp:dp1285
    as

    Download full text from publisher

    File URL: https://www.diw.de/documents/publikationen/73/diw_01.c.417800.de/dp1285.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sahuc, Jean-Guillaume, 2006. "Partial indexation, trend inflation, and the hybrid Phillips curve," Economics Letters, Elsevier, vol. 90(1), pages 42-50, January.
    2. Vasco Cúrdia & Marco Del Negro & Daniel L. Greenwald, 2014. "Rare Shocks, Great Recessions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1031-1052, November.
    3. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    Full references (including those not matched with items on IDEAS)

    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. Karlsson, Sune & Mazur, Stepan & Nguyen, Hoang, 2023. "Vector autoregression models with skewness and heavy tails," Journal of Economic Dynamics and Control, Elsevier, vol. 146(C).
    2. Duncan, Roberto & Martínez-García, Enrique, 2019. "New perspectives on forecasting inflation in emerging market economies: An empirical assessment," International Journal of Forecasting, Elsevier, vol. 35(3), pages 1008-1031.
    3. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    4. Delle Monache, Davide & Petrella, Ivan, 2017. "Adaptive models and heavy tails with an application to inflation forecasting," International Journal of Forecasting, Elsevier, vol. 33(2), pages 482-501.
    5. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pintér, Gábor, 2017. "Forecasting with VAR models: Fat tails and stochastic volatility," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1124-1143.
    6. Bobeica, Elena & Hartwig, Benny, 2023. "The COVID-19 shock and challenges for inflation modelling," International Journal of Forecasting, Elsevier, vol. 39(1), pages 519-539.
    7. Joshua C. C. Chan & Liana Jacobi & Dan Zhu, 2022. "An automated prior robustness analysis in Bayesian model comparison," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(3), pages 583-602, April.
    8. Tamás Kiss & Stepan Mazur & Hoang Nguyen & Pär Österholm, 2023. "Modeling the relation between the US real economy and the corporate bond‐yield spread in Bayesian VARs with non‐Gaussian innovations," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 347-368, March.
    9. Chiu, Ching-Wai (Jeremy) & Mumtaz, Haroon & Pinter, Gabor, 2016. "VAR models with non-Gaussian shocks," LSE Research Online Documents on Economics 86238, London School of Economics and Political Science, LSE Library.
    10. Todd E. Clark & Michael W. McCracken & Elmar Mertens, 2020. "Modeling Time-Varying Uncertainty of Multiple-Horizon Forecast Errors," The Review of Economics and Statistics, MIT Press, vol. 102(1), pages 17-33, March.
    11. Antolín-Díaz, Juan & Drechsel, Thomas & Petrella, Ivan, 2024. "Advances in nowcasting economic activity: The role of heterogeneous dynamics and fat tails," Journal of Econometrics, Elsevier, vol. 238(2).
    12. Michele Lenza & Giorgio E. Primiceri, 2022. "How to estimate a vector autoregression after March 2020," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 688-699, June.
    13. Tamás Kiss & Hoang Nguyen & Pär Österholm, 2021. "Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails," JRFM, MDPI, vol. 14(11), pages 1-17, October.
    14. Mertens, Elmar, 2023. "Precision-based sampling for state space models that have no measurement error," Journal of Economic Dynamics and Control, Elsevier, vol. 154(C).
    15. Davide Delle Monache & Ivan Petrella, 2014. "Adaptive Models and Heavy Tails," Birkbeck Working Papers in Economics and Finance 1409, Birkbeck, Department of Economics, Mathematics & Statistics.
    16. Gong, Xiao-Li & Liu, Xi-Hua & Xiong, Xiong & Zhuang, Xin-Tian, 2019. "Non-Gaussian VARMA model with stochastic volatility and applications in stock market bubbles," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 129-136.
    17. repec:wrk:wrkemf:13 is not listed on IDEAS
    18. Marco Del Negro & Giorgio E. Primiceri, 2015. "Time Varying Structural Vector Autoregressions and Monetary Policy: A Corrigendum," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(4), pages 1342-1345.
    19. Ching-Wai (Jeremy) Chiu & Haroon Mumtaz & Gabor Pinter, 2014. "Fat-tails in VAR Models," Working Papers 714, Queen Mary University of London, School of Economics and Finance.
    20. Andrade, P. & Ghysels, E. & Idier, J., 2012. "Tails of Inflation Forecasts and Tales of Monetary Policy," Working papers 407, Banque de France.
    21. Franta, Michal, 2017. "Rare shocks vs. non-linearities: What drives extreme events in the economy? Some empirical evidence," Journal of Economic Dynamics and Control, Elsevier, vol. 75(C), pages 136-157.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • 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

    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:diw:diwwpp:dp1285. 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: Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/diwbede.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.