IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v30y2014i1p129-143.html
   My bibliography  Save this article

Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters

Author

Listed:
  • Barnett, Alina
  • Mumtaz, Haroon
  • Theodoridis, Konstantinos

Abstract

Evidence from a large and growing body of empirical literature strongly suggests that there have been changes in the inflation and output dynamics in the United Kingdom. The majority of these papers base their results on a class of econometric models that allows for time-variation in the coefficients and volatilities of shocks. While these models have been used extensively for studying evolving dynamics and for structural analysis, there has been little evidence that they are useful for forecasting UK output growth and inflation. This paper attempts to fill this gap by comparing the performances of a wide range of time-varying parameter models in forecasting output growth and inflation. We find that allowing for time-varying parameters can lead to large and statistically significant gains in forecast accuracy.

Suggested Citation

  • Barnett, Alina & Mumtaz, Haroon & Theodoridis, Konstantinos, 2014. "Forecasting UK GDP growth and inflation under structural change. A comparison of models with time-varying parameters," International Journal of Forecasting, Elsevier, vol. 30(1), pages 129-143.
  • Handle: RePEc:eee:intfor:v:30:y:2014:i:1:p:129-143
    DOI: 10.1016/j.ijforecast.2013.06.002
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207013000885
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    2. Malin Adolfson & Jesper Linde & Mattias Villani, 2007. "Forecasting Performance of an Open Economy DSGE Model," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 289-328.
    3. Ferrara, Laurent & Marcellino, Massimiliano & Mogliani, Matteo, 2015. "Macroeconomic forecasting during the Great Recession: The return of non-linearity?," International Journal of Forecasting, Elsevier, vol. 31(3), pages 664-679.
    4. Gon alves, S lvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(06), pages 1367-1384, December.
    5. Timothy Cogley & Thomas J. Sargent, 2008. "Anticipated Utility And Rational Expectations As Approximations Of Bayesian Decision Making," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(1), pages 185-221, February.
    6. Groen, Jan J J & Mumtaz, Haroon, 2008. "Investigating the structural stability of the Phillips curve relationship," Bank of England working papers 350, Bank of England.
    7. Todd E. Clark & Michael W. McCracken, 2009. "Improving Forecast Accuracy By Combining Recursive And Rolling Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(2), pages 363-395, May.
    8. Engemann, Kristie M. & Owyang, Michael T., 2010. "Whatever Happened To The Business Cycle? A Bayesian Analysis Of Jobless Recoveries," Macroeconomic Dynamics, Cambridge University Press, vol. 14(05), pages 709-726, November.
    9. Rossi, Barbara & Sekhposyan, Tatevik, 2010. "Have economic models' forecasting performance for US output growth and inflation changed over time, and when?," International Journal of Forecasting, Elsevier, vol. 26(4), pages 808-835, October.
    10. Timothy Cogley & Thomas J. Sargent, 2002. "Evolving Post-World War II U.S. Inflation Dynamics," NBER Chapters,in: NBER Macroeconomics Annual 2001, Volume 16, pages 331-388 National Bureau of Economic Research, Inc.
    11. Eickmeier, Sandra & Lemke, Wolfgang & Marcellino, Massimiliano, 2011. "Classical time-varying FAVAR models - estimation, forecasting and structural analysis," Discussion Paper Series 1: Economic Studies 2011,04, Deutsche Bundesbank.
    12. Goncalves, Silvia & White, Halbert, 2004. "Maximum likelihood and the bootstrap for nonlinear dynamic models," Journal of Econometrics, Elsevier, vol. 119(1), pages 199-219, March.
    13. Luca Benati & Paolo Surico, 2008. "Evolving U.S. Monetary Policy and The Decline of Inflation Predictability," Journal of the European Economic Association, MIT Press, vol. 6(2-3), pages 634-646, 04-05.
    14. Raffaella Giacomini & Barbara Rossi, 2010. "Forecast comparisons in unstable environments," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 595-620.
    15. Chen, Cathy W. S., 1998. "A Bayesian analysis of generalized threshold autoregressive models," Statistics & Probability Letters, Elsevier, vol. 40(1), pages 15-22, September.
    16. Baumeister, Christiane & Benati, Luca, 2010. "Unconventional monetary policy and the great recession - Estimating the impact of a compression in the yield spread at the zero lower bound," Working Paper Series 1258, European Central Bank.
    17. Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2013. "Changes in the effects of monetary policy on disaggregate price dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 37(3), pages 543-560.
    18. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    19. Jana Eklund & Sune Karlsson, 2007. "Forecast Combination and Model Averaging Using Predictive Measures," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 329-363.
    20. M. Hashem Pesaran & Davide Pettenuzzo & Allan Timmermann, 2006. "Forecasting Time Series Subject to Multiple Structural Breaks," Review of Economic Studies, Oxford University Press, vol. 73(4), pages 1057-1084.
    21. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    22. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 125-132.
    23. Clements, Michael P & Hendry, David F, 1995. "Forecasting in Cointegration Systems," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 10(2), pages 127-146, April-Jun.
    24. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    25. Timothy Cogley & Giorgio E. Primiceri & Thomas J. Sargent, 2010. "Inflation-Gap Persistence in the US," American Economic Journal: Macroeconomics, American Economic Association, vol. 2(1), pages 43-69, January.
    26. Eklund, Jana & Kapetanios, George & Price, Simon, 2010. "Forecasting in the presence of recent structural change," Bank of England working papers 406, Bank of England.
    27. Clark, Todd E. & McCracken, Michael W., 2009. "Tests of Equal Predictive Ability With Real-Time Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 27(4), pages 441-454.
    28. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," Review of Economic Studies, Oxford University Press, vol. 72(3), pages 821-852.
    29. Nathan S. Balke, 2000. "Credit and Economic Activity: Credit Regimes and Nonlinear Propagation of Shocks," The Review of Economics and Statistics, MIT Press, vol. 82(2), pages 344-349, May.
    30. Christopher A. Sims & Tao Zha, 2006. "Were There Regime Switches in U.S. Monetary Policy?," American Economic Review, American Economic Association, vol. 96(1), pages 54-81, March.
    31. Antonello D'Agostino & Luca Gambetti & Domenico Giannone, 2013. "Macroeconomic forecasting and structural change," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(1), pages 82-101, January.
    32. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    33. Chib, Siddhartha, 1998. "Estimation and comparison of multiple change-point models," Journal of Econometrics, Elsevier, vol. 86(2), pages 221-241, June.
    34. James H. Stock & Mark W. Watson, 2003. "How did leading indicator forecasts perform during the 2001 recession?," Economic Quarterly, Federal Reserve Bank of Richmond, issue Sum, pages 71-90.
    35. Haroon Mumtaz & Laura Sunder‐Plassmann, 2013. "Time‐Varying Dynamics Of The Real Exchange Rate: An Empirical Analysis," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(3), pages 498-525, April.
    36. Jeremy Smith & Kenneth F. Wallis, 2009. "A Simple Explanation of the Forecast Combination Puzzle," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(3), pages 331-355, June.
    37. Hedibert F. Lopes & Esther Salazar, 2006. "Bayesian Model Uncertainty In Smooth Transition Autoregressions," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(1), pages 99-117, January.
    38. Baumeister, Christiane & Liu, Philip & Mumtaz, Haroon, 2010. "Changes in the transmission of monetary policy: evidence from a time-varying factor-augmented VAR," Bank of England working papers 401, Bank of England.
    39. Mark W. Watson & James H. Stock, 2004. "Combination forecasts of output growth in a seven-country data set," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 405-430.
    40. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    41. Benati, Luca, 2007. "The "Great Moderation" in the United Kingdom," Working Paper Series 769, European Central Bank.
    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. 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.
    2. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2015. "Was the recent downturn in US real GDP predictable?," Applied Economics, Taylor & Francis Journals, vol. 47(28), pages 2985-3007, June.
    3. Berg, Tim O. & Henzel, Steffen R., 2015. "Point and density forecasts for the euro area using Bayesian VARs," International Journal of Forecasting, Elsevier, vol. 31(4), pages 1067-1095.
    4. Vasilios Plakandaras & Rangan Gupta & Mark E. Wohar, 2017. "An Assessment of UK Macroeconomic Volatility: Historical Evidence Using Over Seven Centuries of Data," Working Papers 201779, University of Pretoria, Department of Economics.
    5. Mawuli Segnon & Rangan Gupta & Stelios Bekiros & Mark E. Wohar, 2016. "Forecasting US GNP Growth: The Role of Uncertainty," Working Papers 201667, University of Pretoria, Department of Economics.
    6. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta, 2017. "International stock return predictability: Is the role of U.S. time-varying?," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 44(1), pages 121-146, February.
    7. Kemal Bagzibagli, 2014. "Monetary transmission mechanism and time variation in the Euro area," Empirical Economics, Springer, vol. 47(3), pages 781-823, November.
    8. Mehmet Balcilar & Rangan Gupta & Anandamayee Majumdar & Stephen M. Miller, 2012. "Was the Recent Downturn in US GDP Predictable?," Working Papers 201230, University of Pretoria, Department of Economics.
    9. Rafael Ravnik, 2014. "Short-Term Forecasting of GDP under Structural Changes," Working Papers 40, The Croatian National Bank, Croatia.
    10. Vugar Ahmadov & Shaig Adigozalov & Salman Huseynov & Fuad Mammadov & Vugar Rahimov, 2016. "Forecasting inflation in post-oil boom years: A case for non-linear models?," Working Papers 1601, Central Bank of Azerbaijan Republic.
    11. Knut Are Aastveit & Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2017. "Have Standard VARS Remained Stable Since the Crisis?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(5), pages 931-951, August.
    12. Mandalinci, Zeyyad, 2017. "Forecasting inflation in emerging markets: An evaluation of alternative models," International Journal of Forecasting, Elsevier, vol. 33(4), pages 1082-1104.
    13. Goodness C. Aye & Mehmet Balcilar & John P. Dunne & Rangan Gupta & Reneé van Eyden, 2014. "Military expenditure, economic growth and structural instability: a case study of South Africa," Defence and Peace Economics, Taylor & Francis Journals, vol. 25(6), pages 619-633, December.
    14. Bekiros, Stelios & Gupta, Rangan & Paccagnini, Alessia, 2015. "Oil price forecastability and economic uncertainty," Economics Letters, Elsevier, vol. 132(C), pages 125-128.
    15. repec:spr:empeco:v:53:y:2017:i:2:d:10.1007_s00181-016-1128-y is not listed on IDEAS
    16. Michael Wickens, 2014. "How Useful are DSGE Macroeconomic Models for Forecasting?," Open Economies Review, Springer, vol. 25(1), pages 171-193, February.
    17. repec:eee:eneeco:v:65:y:2017:i:c:p:411-423 is not listed on IDEAS
    18. Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
    19. Bergmeir, Christoph & Costantini, Mauro & Benítez, José M., 2014. "On the usefulness of cross-validation for directional forecast evaluation," Computational Statistics & Data Analysis, Elsevier, vol. 76(C), pages 132-143.
    20. Garnitz, Johanna & Lehmann, Robert & Wohlrabe, Klaus, 2017. "Forecasting GDP all over the World: Evidence from Comprehensive Survey Data," MPRA Paper 81772, University Library of Munich, Germany.
    21. repec:spr:scient:v:100:y:2014:i:1:d:10.1007_s11192-014-1257-z is not listed on IDEAS

    More about this item

    Keywords

    Time-varying parameters; Regime switching; Vector autoregressions; Forecast comparison;

    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
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

    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:eee:intfor:v:30:y:2014:i:1:p:129-143. 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: (Dana Niculescu). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.