IDEAS home Printed from https://ideas.repec.org/p/sur/surrec/1219.html
   My bibliography  Save this paper

Designing Robust Monetary Policy Using Prediction Pools

Author

Listed:
  • Szabolcs Deák

    (University of Surrey)

  • Paul Levine

    (University of Surrey)

  • Afrasiab Mirza

    (University of Birmingham)

  • Joseph Pearlman

    (City University)

Abstract

How should a forward-looking policy maker conduct monetary policy when she has a finite set of models at her disposal, none of which are believed to be the true data generating process? In our approach, the policy maker first assigns weights to models based on relative forecasting performance rather than in-sample fit, consistent with her forward-looking objective. These weights are then used to solve a policy design problem that selects the optimized Taylor-type interest-rate rule that is robust to model uncertainty across a set of well-established DSGE models with and without financial frictions. We find that the choice of weights has a significant impact on the robust optimized rule which is more inertial and aggressive than either the non-robust single model counterparts or the optimal robust rule based on backward-looking weights as in the common alternative Bayesian Model Averaging. Importantly, we show that a price-level rule has excellent welfare and robustness properties, and therefore should be viewed as a key instrument for policy makers facing uncertainty over the nature of financial frictions.

Suggested Citation

  • Szabolcs Deák & Paul Levine & Afrasiab Mirza & Joseph Pearlman, 2019. "Designing Robust Monetary Policy Using Prediction Pools," School of Economics Discussion Papers 1219, School of Economics, University of Surrey.
  • Handle: RePEc:sur:surrec:1219
    as

    Download full text from publisher

    File URL: https://repec.som.surrey.ac.uk/2019/DP12-19.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Orphanides, Athanasios & Williams, John C., 2008. "Learning, expectations formation, and the pitfalls of optimal control monetary policy," Journal of Monetary Economics, Elsevier, vol. 55(Supplemen), pages 80-96, October.
    2. Levin, Andrew T. & Williams, John C., 2003. "Robust monetary policy with competing reference models," Journal of Monetary Economics, Elsevier, vol. 50(5), pages 945-975, July.
    3. Kapetanios, G. & Mitchell, J. & Price, S. & Fawcett, N., 2015. "Generalised density forecast combinations," Journal of Econometrics, Elsevier, vol. 188(1), pages 150-165.
    4. Schmitt-Grohe, Stephanie & Uribe, Martin, 2007. "Optimal simple and implementable monetary and fiscal rules," Journal of Monetary Economics, Elsevier, vol. 54(6), pages 1702-1725, September.
    5. Dennis, Richard & Leitemo, Kai & Söderström, Ulf, 2009. "Methods for robust control," Journal of Economic Dynamics and Control, Elsevier, vol. 33(8), pages 1604-1616, August.
    6. Brock, William A. & Durlauf, Steven N. & Nason, James M. & Rondina, Giacomo, 2007. "Simple versus optimal rules as guides to policy," Journal of Monetary Economics, Elsevier, vol. 54(5), pages 1372-1396, July.
    7. Orphanides, Athanasios & Williams, John C., 2007. "Robust monetary policy with imperfect knowledge," Journal of Monetary Economics, Elsevier, vol. 54(5), pages 1406-1435, July.
    8. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 1-62.
    9. Martin Ellison & Thomas J. Sargent, 2012. "A Defense Of The Fomc," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 53(4), pages 1047-1065, November.
    10. Sims, Christopher A., 2008. "Improving monetary policy models," Journal of Economic Dynamics and Control, Elsevier, vol. 32(8), pages 2460-2475, August.
    11. Del Negro, Marco & Hasegawa, Raiden B. & Schorfheide, Frank, 2016. "Dynamic prediction pools: An investigation of financial frictions and forecasting performance," Journal of Econometrics, Elsevier, vol. 192(2), pages 391-405.
    12. Marc Carreras & Olivier Coibion & Yuriy Gorodnichenko & Johannes Wieland, 2016. "Infrequent but Long-Lived Zero-Bound Episodes and the Optimal Rate of Inflation," Working Papers id:11216, eSocialSciences.
    13. Giannoni, Marc P., 2014. "Optimal interest-rate rules and inflation stabilization versus price-level stabilization," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 110-129.
    14. Brock, William A. & Durlauf, Steven N. & West, Kenneth D., 2007. "Model uncertainty and policy evaluation: Some theory and empirics," Journal of Econometrics, Elsevier, vol. 136(2), pages 629-664, February.
    15. Binder, Michael & Lieberknecht, Philipp & Quintana, Jorge & Wieland, Volker, 2017. "Model Uncertainty in Macroeconomics: On the Implications of Financial Frictions," CEPR Discussion Papers 12013, C.E.P.R. Discussion Papers.
    16. Hall, Stephen G. & Mitchell, James, 2007. "Combining density forecasts," International Journal of Forecasting, Elsevier, vol. 23(1), pages 1-13.
    17. 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.
    18. Bernanke, Ben S. & Gertler, Mark & Gilchrist, Simon, 1999. "The financial accelerator in a quantitative business cycle framework," Handbook of Macroeconomics,in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 21, pages 1341-1393 Elsevier.
    19. Athanasios Orphanides & John C. Williams, 2002. "Robust Monetary Policy Rules with Unknown Natural Rates," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 63-146.
    20. John Geweke, 2010. "Complete and Incomplete Econometric Models," Economics Books, Princeton University Press, edition 1, number 9218.
    21. Denise Côté & John Kuszczak & Jean-Paul Lam & Ying Liu & Pierre St-Amant, 2004. "The performance and robustness of simple monetary policy rules in models of the Canadian economy," Canadian Journal of Economics, Canadian Economics Association, vol. 37(4), pages 978-998, November.
    22. Pelin Ilbas & Øistein Røisland & Tommy Sveen, 2012. "Robustifying optimal monetary policy using simple rules as cross-checks," Working Paper 2012/22, Norges Bank.
    23. John Geweke & Gianni Amisano, 2012. "Prediction with Misspecified Models," American Economic Review, American Economic Association, vol. 102(3), pages 482-486, May.
    24. 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.
    25. Geweke, John & Amisano, Gianni, 2011. "Optimal prediction pools," Journal of Econometrics, Elsevier, vol. 164(1), pages 130-141, September.
    26. Holden, Thomas, 2016. "Existence and uniqueness of solutions to dynamic models with occasionally binding constraints," EconStor Preprints 130142, ZBW - Leibniz Information Centre for Economics.
    27. Taylor, John B. & Williams, John C., 2010. "Simple and Robust Rules for Monetary Policy," Handbook of Monetary Economics,in: Benjamin M. Friedman & Michael Woodford (ed.), Handbook of Monetary Economics, edition 1, volume 3, chapter 15, pages 829-859 Elsevier.
    28. Schmitt-Grohe, Stephanie & Uribe, Martin, 2000. "Price level determinacy and monetary policy under a balanced-budget requirement," Journal of Monetary Economics, Elsevier, vol. 45(1), pages 211-246, February.
    29. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    30. Levine, Paul & McAdam, Peter & Pearlman, Joseph, 2012. "Probability models and robust policy rules," European Economic Review, Elsevier, vol. 56(2), pages 246-262.
    31. Robert J. Tetlow, 2015. "Real-Time Model Uncertainty in the United States: "Robust" Policies Put to the Test," International Journal of Central Banking, International Journal of Central Banking, vol. 11(2), pages 113-155, March.
    32. Timothy Cogley & Thomas J. Sargent, 2005. "The conquest of US inflation: Learning and robustness to model uncertainty," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 528-563, April.
    33. 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.
    34. Ramón Adalid & Günter Coenen & Peter McAdam & Stefano Siviero, 2005. "The Performance and Robustness of Interest-Rate Rules in Models of the Euro Area," International Journal of Central Banking, International Journal of Central Banking, vol. 1(1), May.
    35. Marc Dordal i Carreras & Olivier Coibion & Yuriy Gorodnichenko & Johannes Wieland, 2016. "Infrequent but Long-Lived Zero Lower Bound Episodes and the Optimal Rate of Inflation," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 497-520, October.
    36. Binder, Michael & Lieberknecht, Philipp & Quintana, Jorge & Wieland, Volker, 2018. "Robust Macroprudential Policy Rules under Model Uncertainty," Annual Conference 2018 (Freiburg, Breisgau): Digital Economy 181503, Verein für Socialpolitik / German Economic Association.
    37. Holden, Tom D., 2016. "Existence, uniqueness and computation of solutions to dynamic models with occasionally binding constraints," EconStor Preprints 127430, ZBW - Leibniz Information Centre for Economics.
    38. Svensson, Lars E O, 1999. "Price-Level Targeting versus Inflation Targeting: A Free Lunch?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 31(3), pages 277-295, August.
    39. Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
    40. Levine, Paul & Currie, David, 1987. "The design of feedback rules in linear stochastic rational expectations models," Journal of Economic Dynamics and Control, Elsevier, vol. 11(1), pages 1-28, March.
    41. McAdam, Peter & Warne, Anders, 2019. "Euro area real-time density forecasting with financial or labor market frictions," International Journal of Forecasting, Elsevier, vol. 35(2), pages 580-600.
    42. Jump, Robert Calvert & Levine, Paul, 2019. "Behavioural New Keynesian models," Journal of Macroeconomics, Elsevier, vol. 59(C), pages 59-77.
    43. Gary Chamberlain, 2000. "Econometric applications of maxmin expected utility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(6), pages 625-644.
    44. Cogley, Timothy & De Paoli, Bianca & Matthes, Christian & Nikolov, Kalin & Yates, Tony, 2011. "A Bayesian approach to optimal monetary policy with parameter and model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 35(12), pages 2186-2212.
    45. Levine, Paul & Pearlman, Joseph, 2010. "Robust monetary rules under unstructured model uncertainty," Journal of Economic Dynamics and Control, Elsevier, vol. 34(3), pages 456-471, March.
    46. Gertler, Mark & Karadi, Peter, 2011. "A model of unconventional monetary policy," Journal of Monetary Economics, Elsevier, vol. 58(1), pages 17-34, January.
    47. Townsend, Robert M., 1979. "Optimal contracts and competitive markets with costly state verification," Journal of Economic Theory, Elsevier, vol. 21(2), pages 265-293, October.
    48. Gertler, Mark & Kiyotaki, Nobuhiro & Queralto, Albert, 2012. "Financial crises, bank risk exposure and government financial policy," Journal of Monetary Economics, Elsevier, vol. 59(S), pages 17-34.
    Full references (including those not matched with items on IDEAS)

    More about this item

    JEL classification:

    • D52 - Microeconomics - - General Equilibrium and Disequilibrium - - - Incomplete Markets
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

    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:sur:surrec:1219. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ioannis Lazopoulos). General contact details of provider: http://edirc.repec.org/data/desuruk.html .

    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 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.