IDEAS home Printed from https://ideas.repec.org/p/aoz/wpaper/165.html
   My bibliography  Save this paper

Priors and the Slope of the Phillips Curve

Author

Listed:
  • Callum Jones

    (Board of Governors of the Federal Reserve System)

  • Mariano Kulish

    (University of Sydney)

  • Juan Pablo Nicolini

    (Federal Reserve Bank of Minneapolis/Universidad Di Tella)

Abstract

The slope of the Phillips curve in New Keynesian models is difficult to estimate using aggregate data. We show that in a Bayesian estimation, the priors placed on the parametersgoverning nominal rigidities significantly influence posterior estimates and thus inferences about the importance of nominal rigidities. Conversely, we show that priors play a negligible role in a New Keynesian model estimated using state-level data. An estimation with state-level data exploits a relatively large panel dataset and removes the influence of endogenous monetary policy

Suggested Citation

  • Callum Jones & Mariano Kulish & Juan Pablo Nicolini, 2022. "Priors and the Slope of the Phillips Curve," Working Papers 165, Red Nacional de Investigadores en Economía (RedNIE).
  • Handle: RePEc:aoz:wpaper:165
    as

    Download full text from publisher

    File URL: https://rednie.eco.unc.edu.ar/files/DT/165.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Terry J. Fitzgerald & Callum J. Jones & Mariano Kulish & Juan Pablo Nicolini, 2020. "Is There a Stable Relationship between Unemployment and Future Inflation?," Staff Report 614, Federal Reserve Bank of Minneapolis.
    2. Canova, Fabio & Sala, Luca, 2009. "Back to square one: Identification issues in DSGE models," Journal of Monetary Economics, Elsevier, vol. 56(4), pages 431-449, May.
    3. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    4. Frank Smets & Rafael Wouters, 2007. "Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach," American Economic Review, American Economic Association, vol. 97(3), pages 586-606, June.
    5. Michael McLeay & Silvana Tenreyro, 2020. "Optimal Inflation and the Identification of the Phillips Curve," NBER Macroeconomics Annual, University of Chicago Press, vol. 34(1), pages 199-255.
    6. Peter N. Ireland, 2004. "Technology Shocks in the New Keynesian Model," The Review of Economics and Statistics, MIT Press, vol. 86(4), pages 923-936, November.
    7. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    8. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    9. Ivana Komunjer & Serena Ng, 2011. "Dynamic Identification of Dynamic Stochastic General Equilibrium Models," Econometrica, Econometric Society, vol. 79(6), pages 1995-2032, November.
    10. Atif Mian & Amir Sufi, 2011. "House Prices, Home Equity-Based Borrowing, and the US Household Leverage Crisis," American Economic Review, American Economic Association, vol. 101(5), pages 2132-2156, August.
    11. Philippon, Thomas & Midrigan, Virgiliu, 2011. "Household Leverage and the Recession," CEPR Discussion Papers 8381, C.E.P.R. Discussion Papers.
    12. Kulish, Mariano & Morley, James & Robinson, Tim, 2017. "Estimating DSGE models with zero interest rate policy," Journal of Monetary Economics, Elsevier, vol. 88(C), pages 35-49.
    13. Gauti B. Eggertsson & Michael Woodford, 2003. "The Zero Bound on Interest Rates and Optimal Monetary Policy," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 34(1), pages 139-235.
    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. Inoue, Atsushi & Kuo, Chun-Hung & Rossi, Barbara, 2020. "Identifying the sources of model misspecification," Journal of Monetary Economics, Elsevier, vol. 110(C), pages 1-18.
    2. Raffaella Giacomini, 2013. "The relationship between DSGE and VAR models," CeMMAP working papers CWP21/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    3. Ho, Paul, 2023. "Global robust Bayesian analysis in large models," Journal of Econometrics, Elsevier, vol. 235(2), pages 608-642.
    4. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    5. Daniel O. Beltran & David Draper, 2018. "Estimating dynamic macroeconomic models: how informative are the data?," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 501-520, February.
    6. Le, Vo Phuong Mai & Meenagh, David & Minford, Patrick & Wickens, Michael, 2017. "A Monte Carlo procedure for checking identification in DSGE models," Journal of Economic Dynamics and Control, Elsevier, vol. 76(C), pages 202-210.
    7. Gorodnichenko, Yuriy & Ng, Serena, 2010. "Estimation of DSGE models when the data are persistent," Journal of Monetary Economics, Elsevier, vol. 57(3), pages 325-340, April.
    8. Ríos-Rull, José-Víctor & Schorfheide, Frank & Fuentes-Albero, Cristina & Kryshko, Maxym & Santaeulàlia-Llopis, Raül, 2012. "Methods versus substance: Measuring the effects of technology shocks," Journal of Monetary Economics, Elsevier, vol. 59(8), pages 826-846.
    9. Nikolay Iskrev, 2010. "Evaluating the strength of identification in DSGE models. An a priori approach," 2010 Meeting Papers 1117, Society for Economic Dynamics.
    10. Adolfson, Malin & Laséen, Stefan & Lindé, Jesper & Ratto, Marco, 2019. "Identification versus misspecification in New Keynesian monetary policy models," European Economic Review, Elsevier, vol. 113(C), pages 225-246.
    11. Callum Jones & Mariano Kulish & Daniel M. Rees, 2022. "International spillovers of forward guidance shocks," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 131-160, January.
    12. Ivashchenko, Sergey & Mutschler, Willi, 2020. "The effect of observables, functional specifications, model features and shocks on identification in linearized DSGE models," Economic Modelling, Elsevier, vol. 88(C), pages 280-292.
    13. Fernández-Villaverde, J. & Rubio-Ramírez, J.F. & Schorfheide, F., 2016. "Solution and Estimation Methods for DSGE Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 527-724, Elsevier.
    14. Terry J. Fitzgerald & Callum J. Jones & Mariano Kulish & Juan Pablo Nicolini, 2020. "Is There a Stable Relationship between Unemployment and Future Inflation?," Staff Report 614, Federal Reserve Bank of Minneapolis.
    15. Adolfson, Malin & Lindé, Jesper, 2011. "Parameter Identification in a Estimated New Keynesian Open Economy Model," Working Paper Series 251, Sveriges Riksbank (Central Bank of Sweden).
    16. Kocięcki, Andrzej & Kolasa, Marcin, 2023. "A solution to the global identification problem in DSGE models," Journal of Econometrics, Elsevier, vol. 236(2).
    17. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    18. Pierre Lafourcade & Joris de Wind, 2012. "Taking Trends Seriously in DSGE Models: An Application to the Dutch Economy," DNB Working Papers 345, Netherlands Central Bank, Research Department.
    19. Mutschler, Willi, 2018. "Higher-order statistics for DSGE models," Econometrics and Statistics, Elsevier, vol. 6(C), pages 44-56.
    20. Piotr Ciżkowicz & Andrzej Rzońca & Andrzej Torój, 2019. "In Search of an Appropriate Lower Bound. The Zero Lower Bound vs. the Positive Lower Bound under Discretion and Commitment," German Economic Review, Verein für Socialpolitik, vol. 20(4), pages 1028-1053, November.

    More about this item

    Keywords

    Slope of the Phillips curve; priors; Bayesian estimation; state-level data;
    All these keywords.

    JEL classification:

    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • E58 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Central Banks and Their Policies

    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:aoz:wpaper:165. 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: Laura Inés D Amato (email available below). General contact details of provider: https://edirc.repec.org/data/redniar.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.