Output gaps, inflation and financial cycles in the UK

  • Marko MelolinnaEmail author
  • Máté Tóth


This paper aims at constructing potential output and output gap measures for the UK which are pinned down by macroeconomic relationships as well as financial indicators. The exercise is based on a parsimonious unobserved components model which is estimated via Bayesian methods where the time-paths of unobserved variables are extracted with the Kalman filter. The resulting measures track current narratives on macroeconomic cycles and trends in the UK reasonably well. The inclusion of summary indicators of financial conditions leads to a more optimistic view on the path of UK potential output after the crisis and adds value to the model via improving its real-time performance. The models augmented with financial conditions have some real-time wage inflation forecasting ability over the monetary policy-relevant 2- to 3-year horizon during the last 15 years. Finally, we also introduce a new approach to construct financial conditions indices, with emphasis on their real-time performance and ability to track the evolution of macro-financial imbalances. Our results can be relevant from both monetary and macro-prudential policy perspectives.


Bayesian estimation Business cycle Forecasting Financial conditions Real-time data Unobserved components model 

JEL Classification

C11 C32 E31 E32 E52 

Supplementary material (37 kb)
Supplementary material 1 (ZIP 38 kb) (6 kb)
Supplementary material 2 (ZIP 6 kb) (1 kb)
Supplementary material 3 (ZIP 1 kb)


  1. Andrle M (2013) What is in your output gap? Unified framework & decomposition into observables. IMF Working Paper No. 105 (May 2013)Google Scholar
  2. Arseneau DM, Kiley M (2014) The role of financial imbalances in assessing the state of the economy. FEDS Notes, April 18 2014Google Scholar
  3. Benes J, Clinton K, Garcia-Saltos R, Johnson M, Laxton D, Manchev P, Matheson T (2010) Estimating potential output with a multivariate filter. IMF Working Paper No 285 (December 2010)Google Scholar
  4. Bernanke BS, Gertler M, Gilchrist S (1999) The financial accelerator in a quantitative business cycle framework. In: Taylor JB, Woodford M (eds) Handbook of macroeconomics, vol 1, Part C. Elsevier, Amsterdam, pp 1341–1393CrossRefGoogle Scholar
  5. Blagrave P, Garcia-Saltos R, Laxton D, Zhang F (2015) A simple multivariate filter for estimating potential output. IMF Working Paper 15/79Google Scholar
  6. Borio C, Disyatat P, Juselius M (2013) Rethinking potential output: embedding information about the financial cycle. BIS Working Papers No 404Google Scholar
  7. Borio C, Disyatat P, Juselius M (2014) A parsimonious approach to incorporating economic information in measures of potential output. BIS Working Papers No 442Google Scholar
  8. Darracq-Paries M, Maurin L, Moccero D (2014) Financial conditions index and credit supply shocks for the euro area. ECB Working Paper No. 1644Google Scholar
  9. Darvas Z, Simon A (2015) Filling the gap: open economy considerations for more reliable potential output estimates. Bruegel Working Paper 2015/11Google Scholar
  10. Durbin J, Koopman SJ (2001) Time series analysis by state space methods. Oxford University Press, OxfordGoogle Scholar
  11. Fernald J (2012) Productivity and potential output before, during and after the great recession. Federal Reserve Bank of San Francisco Working Paper 2012-18Google Scholar
  12. Fisher I (1933) The debt-deflation theory of great depressions. Econometrica 1(4):337–357CrossRefGoogle Scholar
  13. Gali J (2010) The return of the wage Phillips curve. NBER Working Paper No. 15758 (February 2010)Google Scholar
  14. Gertler M, Karadi P (2011) A model of unconventional monetary policy. J Monet Econ 58(1):17–34CrossRefGoogle Scholar
  15. Grant A, Chan J (2017) A Bayesian model comparison for trend-cycle decompositions of output. J Money Credit Bank 49:525–552CrossRefGoogle Scholar
  16. Guichard S, Haugh D, Turner D (2009) Quantifying the effect of financial conditions in the euro area, Japan, United Kingdom and United States. OECD Economics Department Working Papers No. 677Google Scholar
  17. Harvey A, Koopman SJ, Penzer J (1998) Messy time series: a united approach. Adv Econom 13:103–143CrossRefGoogle Scholar
  18. Havik K, McMorrow K, Orlandi F, Planas C, Raciborski R, Roeger W, Rossi A, Thum-Thysen A, Vandermeulenet V (2014) The production function methodology for calculating potential growth rates & output gaps. European Commission Economic Papers No 535Google Scholar
  19. Iacoviello M (2005) House prices, borrowing constraints, and monetary policy in the business cycle. Am Econ Rev 95(3):739–764CrossRefGoogle Scholar
  20. Jarocinski M, Lenza M (2016) An inflation-predicting measure of the output gap in the euro area. ECB Working Paper No 1966Google Scholar
  21. Jorda O, Schularick M, Taylor AM (2016) Macrofinancial history and the new business cycle facts. NBER Macroecon Annu 31:213–263CrossRefGoogle Scholar
  22. Kalman R (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82(1):35–45CrossRefGoogle Scholar
  23. Kiyotaki N, Moore J (1997) Credit cycles. J Polit Econ 105:211–248CrossRefGoogle Scholar
  24. Kuttner K (1994) Estimating potential output as a latent variable. J Bus Econ Stat 12(3):361–368Google Scholar
  25. Luo S, Startz R (2014) Is it one break or ongoing permanent shocks that explains US real GDP? J Monet Econ 66:155–163CrossRefGoogle Scholar
  26. Matheson T (2012) Financial conditions indexes for the United States and Euro area. Econ Lett 115(3):441–446CrossRefGoogle Scholar
  27. OBR (2014) Output gap measurement: judgement and uncertainty. Working paper No 5Google Scholar
  28. Okun A (1962) Potential GDP, its measurement and significance. Cowles Foundation, Yale University, New HevenGoogle Scholar
  29. Orphanides A (2003) Monetary policy evaluation with noisy information. J Monet Econ 50(3):605–631CrossRefGoogle Scholar
  30. Orphanides A, van Norden S (2002) The unreliability of output gap estimates in real time. Rev Econ Stat 84:569–583CrossRefGoogle Scholar
  31. Phillips AW (1958) The relationship between unemployment and the rate of change of money wages in the United Kingdom 1861–1957. Economica 25(November):283–299Google Scholar
  32. Reinhart CM, Rogoff KS (2009) The aftermath of financial crises. Am Econ Rev 99(2):466–472CrossRefGoogle Scholar
  33. Robert CP, Casella G (2004) Monte Carlo statistical methods, 2nd edn. Springer, BerlinCrossRefGoogle Scholar
  34. Rusticelli E (2014) Rescuing the Phillips curve: making use of long-term unemployment in the measurement of the NAIRU. OECD J Econ Stud 2014(1):109–127Google Scholar
  35. Speigner B (2014) Long-term unemployment and convexity in the Phillips curve. Bank of England Working Paper No. 519Google Scholar
  36. Stock JH, Watson MW (1991) A probability model of the coincident economic indicators. In: Lahiri K, Moore G (eds) Leading economic indicators: new approaches and forecasting records, Chap. 4. Cambridge University Press, CambridgeGoogle Scholar
  37. Stock JH, Watson MW (1998) Median unbiased estimation of coefficient variance in a time-varying parameter model. J Am Stat Assoc 93(441):349–358CrossRefGoogle Scholar
  38. Tóth M (2015) Measuring the cyclical position of the Hungarian economy: a multivariate unobserved components model. Mimeo, New YorkGoogle Scholar
  39. van Norden S, Orphanides A (2004) The reliability of inflation forecasts based on output gap estimates in real time. FEDS Working Paper No. 2004-68Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Bank of EnglandLondonUK
  2. 2.European Central BankFrankfurt Am MainGermany

Personalised recommendations