IDEAS home Printed from https://ideas.repec.org/p/ecb/ecbwps/20202404.html
   My bibliography  Save this paper

Monetary policy with judgment

Author

Listed:
  • Gelain, Paolo
  • Manganelli, Simone

Abstract

Two approaches are considered to incorporate judgment in DSGE models. First, Bayesian estimation indirectly imposes judgment via priors on model parameters, which are then mapped into a judgmental interest rate decision. Standard priors are shown to be associated with highly unrealistic judgmental decisions. Second, judgmental interest rate decisions are directly provided by the decision maker, and incorporated into a formal statistical decision rule using frequentist procedures. When the observed interest rates are interpreted as judgmental decisions, they are found to be consistent with DSGE models for long stretches of time, but excessively tight in the 1980s and late 1990s and excessively loose in the late 1970s and early 2000s. JEL Classification: E50, E58, E47, C12, C13

Suggested Citation

  • Gelain, Paolo & Manganelli, Simone, 2020. "Monetary policy with judgment," Working Paper Series 2404, European Central Bank.
  • Handle: RePEc:ecb:ecbwps:20202404
    Note: 196912
    as

    Download full text from publisher

    File URL: https://www.ecb.europa.eu//pub/pdf/scpwps/ecb.wp2404~bce81d28a5.en.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. Mark Gertler & Jordi Gali & Richard Clarida, 1999. "The Science of Monetary Policy: A New Keynesian Perspective," Journal of Economic Literature, American Economic Association, vol. 37(4), pages 1661-1707, December.
    3. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464.
    4. Andrew T. Levin & Alexei Onatski & John Williams & Noah M. Williams, 2006. "Monetary Policy under Uncertainty in Micro-Founded Macroeconometric Models," NBER Chapters, in: NBER Macroeconomics Annual 2005, Volume 20, pages 229-312, National Bureau of Economic Research, Inc.
    5. Rochelle M. Edge & Thomas Laubach & John C. Williams, 2010. "Welfare-maximizing monetary policy under parameter uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 129-143.
    6. Francesco Furlanetto & Paolo Gelain & Marzie Taheri Sanjani, 2021. "Output Gap, Monetary Policy Trade-offs, and Financial Frictions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 52-70, July.
    7. Bodenstein, Martin & Zhao, Junzhu, 2020. "Employment, wages and optimal monetary policy," Journal of Monetary Economics, Elsevier, vol. 112(C), pages 77-96.
    8. Christopher A. Sims, 2001. "Pitfalls of a Minimax Approach to Model Uncertainty," American Economic Review, American Economic Association, vol. 91(2), pages 51-54, May.
    9. Jarociński, Marek & Marcet, Albert, 2019. "Priors about observables in vector autoregressions," Journal of Econometrics, Elsevier, vol. 209(2), pages 238-255.
    10. Lars E O Svensson, 2005. "Monetary Policy with Judgment: Forecast Targeting," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    11. Chen, Xiaoshan & Kirsanova, Tatiana & Leith, Campbell, 2017. "How optimal is US monetary policy?," Journal of Monetary Economics, Elsevier, vol. 92(C), pages 96-111.
    12. Alejandro Justiniano & Giorgio E. Primiceri & Andrea Tambalotti, 2013. "Is There a Trade-Off between Inflation and Output Stabilization?," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(2), pages 1-31, April.
    13. 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.
    14. Negro, Marco Del & Schorfheide, Frank, 2013. "DSGE Model-Based Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 57-140, Elsevier.
    15. Lawrence J. Christiano & Roberto Motto & Massimo Rostagno, 2014. "Risk Shocks," American Economic Review, American Economic Association, vol. 104(1), pages 27-65, January.
    16. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    17. Ireland, Peter N., 2004. "A method for taking models to the data," Journal of Economic Dynamics and Control, Elsevier, vol. 28(6), pages 1205-1226, March.
    18. Ricardo Reis, 2009. "Optimal Monetary Policy Rules in an Estimated Sticky-Information Model," American Economic Journal: Macroeconomics, American Economic Association, vol. 1(2), pages 1-28, July.
    19. 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.
    20. J. B. Taylor & Harald Uhlig (ed.), 2016. "Handbook of Macroeconomics," Handbook of Macroeconomics, Elsevier, edition 1, volume 2, number 2.
    21. Faust, Jon & Gupta, Abhishek, 2010. "Posterior Predictive Analysis for Evaluating DSGE Models," MPRA Paper 26721, University Library of Munich, Germany.
    22. Lombardi, Marco J. & Nicoletti, Giulio, 2012. "Bayesian prior elicitation in DSGE models: Macro- vs micropriors," Journal of Economic Dynamics and Control, Elsevier, vol. 36(2), pages 294-313.
    23. Iskrev, Nikolay, 2010. "Local identification in DSGE models," Journal of Monetary Economics, Elsevier, vol. 57(2), pages 189-202, March.
    24. 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.
    25. 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.
    26. Manganelli, Simone, 2009. "Forecasting With Judgment," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 553-563.
    27. Boneva, Lena & Fawcett, Nicholas & Masolo, Riccardo M. & Waldron, Matt, 2019. "Forecasting the UK economy: Alternative forecasting methodologies and the role of off-model information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 100-120.
    28. Domit, Sílvia & Monti, Francesca & Sokol, Andrej, 2019. "Forecasting the UK economy with a medium-scale Bayesian VAR," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1669-1678.
    29. Francesca Monti, 2010. "Combining Judgment and Models," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(8), pages 1641-1662, December.
    30. John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218.
    31. Mickelsson, Glenn, 2015. "Estimation of DSGE models: Maximum Likelihood vs. Bayesian methods," Working Paper Series 2015:6, Uppsala University, Department of Economics.
    32. Chris Woolston, 2014. "Rice," Nature, Nature, vol. 514(7524), pages 49-49, October.
    33. Frank Smets & Raf Wouters, 2002. "Monetary policy in an estimated stochastic dynamic general equilibrium model of the Euro area," Proceedings, Federal Reserve Bank of San Francisco, issue Mar.
    34. Francesco Furlanetto & Paolo Gelain & Marzie Taheri Sanjani, 2021. "Output Gap, Monetary Policy Trade-offs, and Financial Frictions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 52-70, July.
    35. G. Elliott & C. Granger & A. Timmermann (ed.), 2013. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 2, number 2.
    36. Adjemian, Stéphane & Darracq Pariès, Matthieu & Smets, Frank, 2008. "A quantitative perspective on optimal monetary policy cooperation between the US and the euro area," Working Paper Series 884, European Central Bank.
    37. Smets, Frank & Wouters, Raf, 2002. "An estimated stochastic dynamic general equilibrium model of the euro area," Working Paper Series 171, European Central Bank.
    38. Taylor, John B., 1993. "Discretion versus policy rules in practice," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 39(1), pages 195-214, December.
    39. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
    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. Manganelli, Simone, 2021. "Statistical decision functions with judgment," Working Paper Series 2512, European Central Bank.

    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. 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.
    2. Lindé, Jesper & Smets, Frank & Wouters, Rafael, 2016. "Challenges for Central Banks´ Macro Models," Working Paper Series 323, Sveriges Riksbank (Central Bank of Sweden).
    3. Chatelain, Jean-Bernard & Ralf, Kirsten, 2018. "Publish and Perish: Creative Destruction and Macroeconomic Theory," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 46(2), pages 65-101.
    4. Marco Del Negro & Frank Schorfheide, 2009. "Monetary Policy Analysis with Potentially Misspecified Models," American Economic Review, American Economic Association, vol. 99(4), pages 1415-1450, September.
    5. Paccagnini, Alessia, 2017. "Dealing with Misspecification in DSGE Models: A Survey," MPRA Paper 82914, University Library of Munich, Germany.
    6. Lindé, J. & Smets, F. & Wouters, R., 2016. "Challenges for Central Banks’ Macro Models," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 2185-2262, Elsevier.
    7. Claudia Foroni & Paolo Gelain & Massimiliano Marcellino, 2022. "The financial accelerator mechanism: does frequency matter?," Working Papers 22-29, Federal Reserve Bank of Cleveland.
    8. 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.
    9. Francesco Furlanetto & Paolo Gelain & Marzie Taheri Sanjani, 2021. "Output Gap, Monetary Policy Trade-offs, and Financial Frictions," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 41, pages 52-70, July.
    10. Lawrence J. Christiano & Martin S. Eichenbaum & Mathias Trabandt, 2018. "On DSGE Models," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 113-140, Summer.
    11. 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.
    12. Eric M. Leeper & Nora Traum & Todd B. Walker, 2017. "Clearing Up the Fiscal Multiplier Morass," American Economic Review, American Economic Association, vol. 107(8), pages 2409-2454, August.
    13. Nalan Baştürk & Cem Çakmakli & S. Pinar Ceyhan & Herman K. Van Dijk, 2014. "Posterior‐Predictive Evidence On Us Inflation Using Extended New Keynesian Phillips Curve Models With Non‐Filtered Data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(7), pages 1164-1182, November.
    14. Nalan Basturk & Cem Cakmakli & Pinar Ceyhan & Herman K. van Dijk, 2013. "Posterior-Predictive Evidence on US Inflation using Extended Phillips Curve Models with non-filtered Data," Koç University-TUSIAD Economic Research Forum Working Papers 1321, Koc University-TUSIAD Economic Research Forum.
    15. Hess Chung & Edward Herbst & Michael T. Kiley, 2015. "Effective Monetary Policy Strategies in New Keynesian Models: A Reexamination," NBER Macroeconomics Annual, University of Chicago Press, vol. 29(1), pages 289-344.
    16. Luca Sala, 2015. "Dsge Models in the Frequency Domains," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(2), pages 219-240, March.
    17. Galvão, Ana Beatriz & Giraitis, Liudas & Kapetanios, George & Petrova, Katerina, 2016. "A time varying DSGE model with financial frictions," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 690-716.
    18. Jordi Galí, 2018. "The State of New Keynesian Economics: A Partial Assessment," Journal of Economic Perspectives, American Economic Association, vol. 32(3), pages 87-112, Summer.
    19. Schmidt, Sebastian & Wieland, Volker, 2013. "The New Keynesian Approach to Dynamic General Equilibrium Modeling: Models, Methods and Macroeconomic Policy Evaluation," Handbook of Computable General Equilibrium Modeling, in: Peter B. Dixon & Dale Jorgenson (ed.), Handbook of Computable General Equilibrium Modeling, edition 1, volume 1, chapter 0, pages 1439-1512, Elsevier.
    20. 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.

    More about this item

    Keywords

    DSGE; maximum likelihood; monetary policy; statistical decision theory;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • E50 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - General
    • 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:ecb:ecbwps:20202404. 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: Official Publications (email available below). General contact details of provider: https://edirc.repec.org/data/emieude.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.