International Economics and Economic Policy

, Volume 6, Issue 4, pp 391–419 | Cite as

Ghostbusting: which output gap really matters?

  • Andreas Billmeier
Original Paper


Reflecting domestic demand pressures, the output gap has important implications for economic analysis. This paper assesses the usefulness of four commonly-used gap measures for a small set of European countries. The main results are that the policy implications can be very different depending on the gap measure and that, consequently, care should be exercised when employing any such measure. Moreover the paper investigates in a simple inflation forecasting framework the common assertion that the output gap could improve the forecasting accuracy. For annual observations, however, these measures rarely provide useful information and there is no single best measure across countries.


Output gap Potential output Inflation forecasting 


E31 E32 E37 


  1. Artis M, Marcellino M, Proietti T (2005) Dating the Euro area business cycle. In: Reichlin L (ed) The Euro area business cycle: stylized facts and measurement issues. Centre for Economic Policy Research, London, pp 7–34Google Scholar
  2. Artus JR (1977) Measures of potential output in manufacturing for eight industrial countries, 1955–78. IMF Staff Pap 24:1–35Google Scholar
  3. Baxter M, King R (1999) Measuring business cycles: approximate band-pass filters for economic time series. Rev Econ Stat 81:575–593CrossRefGoogle Scholar
  4. Begg D, Canova F, De Grauwe P, Fatas A, Lane PR (2002) MECB update December 2002. Centre for Economic Policy Research, LondonGoogle Scholar
  5. Berger H, Billmeier A (2003) Estimating the output gap in Finland in: Finland: selected issues, IMF staff country report 03/326. International Monetary Fund, WashingtonGoogle Scholar
  6. Beveridge S, Nelson CR (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘business cycle’. J Monet Econ 7:151–174CrossRefGoogle Scholar
  7. Billmeier A (2004) Ghostbusting: which output gap measure really matters? IMF Working Paper 04/146Google Scholar
  8. Billmeier A (2006) Measuring a roller coaster: evidence on the Finnish output gap. Finn Econ Pap 19(2):69–83Google Scholar
  9. Blanchard O, Quah D (1989) The dynamic effects of aggregate demand and supply disturbances. Am Econ Rev 79:655–673Google Scholar
  10. Bolt W, van Els PJA (2000) Output gap and inflation in the EU. De Nederlandsche Bank Staff Report No. 44, De Nederlandsche Bank, AmsterdamGoogle Scholar
  11. Burns AF, Mitchell WC (1946) Measuring business cycles. National Bureau of Economic Research, New YorkGoogle Scholar
  12. Canova F (1999) Does detrending matter for the determination of the reference cycles and selection of turning points? Econ J 109:126–150CrossRefGoogle Scholar
  13. Cerra V, Saxena SC (2000) Alternative methods of estimating potential output and the output gap: an application to Sweden. IMF Working Paper 00/59Google Scholar
  14. Chadha JS, Nolan C (2004) Output, inflation, and the new Keynesian Phillips curve. Int Rev Appl Econ 18:271–287CrossRefGoogle Scholar
  15. Christiano LJ, Fitzgerald TJ (2003) The band pass filter. Int Econ Rev 44:435–465CrossRefGoogle Scholar
  16. Claus I, Conway P, Scott A (2000) The output gap: measurement, comparisons, and assessment. Reserve Bank of New Zealand Research Paper 44Google Scholar
  17. Cogley T, Nason J (1995) Effects of the Hodrick-Prescott filter on trend and difference stationary time series: implication for business cycle research. J Econ Dyn Control 19:253–278CrossRefGoogle Scholar
  18. Corbae D, Ouliaris S (2006) Extracting cycles from non-stationary data. In: Corbae P, Durlauf N, Hansen B (eds) Econometric theory and practice: frontiers of analysis and applied research—a collection of essays written in honor of Prof. Peter C.B. Phillips. Cambridge University Press, Cambridge, pp 167–177Google Scholar
  19. Cotis J-P, Elmeskov J, Mourougane (2005) Estimates of potential output: benefits and pitfalls from a policy perspective. In: Reichlin L (ed) The Euro area business cycle: stylized facts and measurement issues. Centre for Economic Policy Research, London, pp 35–60Google Scholar
  20. Denis C, Mc Morrow K, Roeger W (2002) Production function approach to calculating potential growth and output gaps—estimates for the EU Member States and the US. European Commission Economic Papers No. 176Google Scholar
  21. Diebold FX (2001) Elements of Forecasting, 2nd ed. South-Western, CincinnatiGoogle Scholar
  22. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13:134–144CrossRefGoogle Scholar
  23. ECB/European Central Bank (2000) Potential Output Growth and Output Gaps: Concepts, Uses, and Estimates. Monthly Bulletin, JuneGoogle Scholar
  24. European Commission (2001) Report on potential output and the output gap. Available at
  25. Everaert L, Nadal-De Simone F (2003) Capital operating time and total factor productivity. IMF Working Paper 03/128Google Scholar
  26. Feldman RA, Berger H, Billmeier A, Kuijs L (2003) Finland: Staff Report for the 2003 Article IV Consultation, IMF Staff Country Report No. 03/325 International Monetary Fund, WashingtonGoogle Scholar
  27. FRB/Board of Governors of the Federal Reserve System (2004) Monetary policy report to the Congress. Federal Reserve Board, WashingtonGoogle Scholar
  28. Gerlach S, Svensson LEO (2003) Money and inflation in the euro area: a case for monetary indicators? J Monet Econ 50:1649–1672CrossRefGoogle Scholar
  29. Goldberg PK, Knetter MM (1997) Goods prices and exchange rates: what have we learned? J Econ Lit 35:1243–1272Google Scholar
  30. Grimm BT, Parker RP (1998) Reliability of the quarterly and annual estimates of GDP and gross domestic income. Surv Curr Bus 78:12–21Google Scholar
  31. Harding D, Pagan A (2006) Synchronization of cycles. J Econom 127:59–79CrossRefGoogle Scholar
  32. Harvey AC, Jaeger A (1993) Detrending, stylized facts and the business cycle. J Appl Econ 8:231–247CrossRefGoogle Scholar
  33. Hodrick RJ, Prescott EC (1997) Post-war U.S. business cycles: an empirical Investigation. J Money Credit Bank 29:1–16CrossRefGoogle Scholar
  34. King RG, Rebelo S (1993) Low frequency filtering and real business cycles. J Econ Dyn Control 17:207–231Google Scholar
  35. Kuttner KN (1994) Estimating potential output as a latent variable. J Bus Econ Stat 12:361–368CrossRefGoogle Scholar
  36. Maravall A, del Rio A (2007) Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter. Comput Stat Data Anal 52:975–998CrossRefGoogle Scholar
  37. McCracken MW (2000) Robust out-of-sample inference. J Econom 99:195–223CrossRefGoogle Scholar
  38. Meese RA, Rogoff K (1983) Empirical exchange rate models of the seventies: do they fit out-of-sample? J Int Econ 14:3–24CrossRefGoogle Scholar
  39. Okun AM (1962) Potential GDP: Its Measurement and Significations. Cowles Foundation Paper 190, 1962 Proceedings of the Business and Economic Statistics Section of the American Statistical Association. Reprinted in Okun AM (1970) The Political Economy of Prosperity. Norton, New YorkGoogle Scholar
  40. Orphanides A (2001) Monetary policy rules based on real-time data. Am Econ Rev 91:964–985CrossRefGoogle Scholar
  41. Orphanides A, van Norden S (2002) The unreliability of output gap estimates in real time. Rev Econ Stat 84:569–583CrossRefGoogle Scholar
  42. Planas C, Rossi A (2003) Program GAP—Version 2.2, Technical Appendix. Unpublished, European Commission, Joint Research Centre, Ispra (Italy)Google Scholar
  43. Proietti T, Musso A, Westermann T (2007) Estimating potential output and the output gap for the Euro Area: a model-based production function approach. Empir Econ 33:85–113CrossRefGoogle Scholar
  44. Ravn MO, Uhlig H (2002) On adjusting the Hodrick-Prescott filter for the frequency of observations. Rev Econ Stat 84:371–380CrossRefGoogle Scholar
  45. Robinson T, Stone A, van Zyl M (2003) The real-time forecasting performance of Phillips curves. Reserve Bank of Australia Research Discussion Paper 2003–12, Reserve Bank of Australia, SydneyGoogle Scholar
  46. Ross K, Ubide A (2001) Mind the gap: what is the best measure of slack in the Euro area? IMF Working Paper 01/203Google Scholar
  47. Scacciavillani F, Swagel P (1999) Measures of potential output: an application to Israel. IMF Working Paper 99/96Google Scholar
  48. Schreiber S, Wolters J (2007) The long-run Phillips curve revisited: is the NAIRU framework data-consistent? J Macroecon 29:355–367CrossRefGoogle Scholar
  49. Stock JH, Watson MW (1989) New indexes of coincident and leading indicators. In: Blanchard OJ, Fischer S (eds) NBER macroeconomics annual. MIT, Cambridge, pp 351–394Google Scholar
  50. Stock JH, Watson MW (1991) A probability model of the coincident economic indicators. In: Lahiri K, Moore GH (eds) Leading economic indicators: new approaches and forecasting records. Cambridge University Press, Cambridge, pp 63–90Google Scholar
  51. Stock JH, Watson MW (1999) Forecasting inflation. J Monet Econ 44:293–335CrossRefGoogle Scholar
  52. Svensson LEO (1999) Inflation targeting as a monetary policy rule. J Monet Econ 43:607–654CrossRefGoogle Scholar
  53. Svensson LEO (2003) What is wrong with Taylor rules? Using judgment in monetary policy rules through targeting rules. J Econ Lit 41:426–477CrossRefGoogle Scholar
  54. Taylor JB (1993) Discretion versus policy rules in practice. Carnegie-Rochester Conf Ser Public Pol 39:195–214CrossRefGoogle Scholar
  55. Theil H (1971) Principles of econometrics. Wiley, New YorkGoogle Scholar
  56. West KD (1996) Asymptotic inference about predictive ability. Econometrica 64:1067–1084CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  1. 1.Middle East and Central Asia DepartmentInternational Monetary Fund (IMF)WashingtonUSA

Personalised recommendations