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Empirical Economics

, Volume 35, Issue 3, pp 413–436 | Cite as

Forecasting inflation with an uncertain output gap

  • Hilde C. BjørnlandEmail author
  • Leif Brubakk
  • Anne Sofie Jore
Original Paper

Abstract

The output gap is a crucial concept in the monetary policy framework, indicating demand pressure that generates inflation. However, its definition and estimation raise a number of theoretical and empirical questions. This paper evaluates a series of univariate and multivariate methods for extracting the output gap in Norway, and compares their value added in predicting inflation. We find that models including the output gap have better predictive power than models based on alternative indicators, and they forecast significantly better than simple benchmark models. Furthermore multivariate measures of the output gap perform better than the univariate gaps.

Keywords

Output gap Forecast Phillips curve Forecast combination 

JEL Classification

C32 E31 E32 E37 

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References

  1. 1.
    Apel M and Jansson P (1999). System estimates of potential output and the NAIRU. Empir Econ 24: 373–388 CrossRefGoogle Scholar
  2. 2.
    Ashley R (2003). Statistically significant forecasting improvements: how much out-of-sample data is likely necessary. Int J Forecast 13: 229–239 CrossRefGoogle Scholar
  3. 3.
    Banjeree A and Marcellino M (2006). Are there any reliable leading indicators for US inflation and GDP. Int J Forecast 22: 137–151 CrossRefGoogle Scholar
  4. 4.
    Baxter M and King R (1999). Measuring business cycles: approximate band-pass filters for economic time series. Rev Econ Stat 81: 575–593 CrossRefGoogle Scholar
  5. 5.
    Bernhardsen T, Eitrheim Ø, Jore AS, Røisland Ø (2004) Real-time data for Norway: challenges for monetary policy. Discussion Paper 26/2004, Deutsche BundesbankGoogle Scholar
  6. 6.
    Beveridge S and Nelson CR (1981). A new approach to the decomposition of economic time series into permanent and transitory components with particular attention to measurement of the ‘Business Cycle’. J Monet Econ 7: 151–174 CrossRefGoogle Scholar
  7. 7.
    Billmeier A (2004) Ghostbusting: which output gap measure really matters? IMF Working Paper 04/146Google Scholar
  8. 8.
    Bjørnland H (2000). Detrending methods and stylized facts of business cycles in Norway—an international comparison. Empir Econ 25: 369–392 CrossRefGoogle Scholar
  9. 9.
    Blanchard OJ and Quah D (1989). The dynamic effects of aggregate demand and supply disturbances. Am Econ Rev 79: 655–673 Google Scholar
  10. 10.
    Burns AF and Mitchell WC (1946). Measuring business cycles. NBER, New York Google Scholar
  11. 11.
    Camba-Mendez G and Rodriguez-Palenzuela D (2003). Assessment criteria for output gap estimates. Econ Modell 20: 529–562 CrossRefGoogle Scholar
  12. 12.
    Canova F (1998). Detrending and business cycle facts. J Monet Econ 41: 475–512 CrossRefGoogle Scholar
  13. 13.
    Cecchetti SG, Chu RS, Steindel C (2000) The unreliability of inflation indicators. Current issues in economics and finance 4/6, Federal Reserve Bank of New YorkGoogle Scholar
  14. 14.
    Christiano LJ, Fitzgerald TJ (1999) The Band Pass Filter. NBER Working Paper No. W7257Google Scholar
  15. 15.
    Clark P (1987). The cyclical component of U.S. economic activity. Q J Econ 102: 797–814 CrossRefGoogle Scholar
  16. 17.
    Clark TE and Mcacken MW (2001). Tests of equal forecast accuracy and en-compassing for nested models. J Econom 105: 85–110 CrossRefGoogle Scholar
  17. 16.
    Clark TE and Mcacken MW (2006). The predictive content of the output gap for inflation: resolving in-sample and out-of-sample evidence. J Money edit Bank 38: 1127–1148 CrossRefGoogle Scholar
  18. 18.
    Clark TE, West KD (2006) Approximately normal tests for equal predictive accuracy in nested models. J Econom (forthcoming)Google Scholar
  19. 19.
    Diebold FX and Mariano RS (1995). Comparing predictive accuracy. J Bus Econ Stat 13: 134–144 CrossRefGoogle Scholar
  20. 20.
    Ehrmann M and Smets F (2003). Uncertain potential output: implications for monetary policy. J Econ Dyn Control 27: 1611–1638 CrossRefGoogle Scholar
  21. 21.
    Frøyland E and Nymoen R (2000). Output gap in the Norwegian economy—different methodologies, same result?. Econ Bull 2: 46–52 Google Scholar
  22. 22.
    Gali J, Rabanal P (2004) Technology shocks and aggregate fluctuations: how well does the RBC model fit postwar U.S. data? In: Gertler M, Rogoff K (eds) NBER macroeconomics annual, pp 225-288Google Scholar
  23. 23.
    Gerlach S and Svensson LEO (2003). Money and inflation in the Euro area: a case for monetary indicators. J Monet Econ 50: 1649–1672 CrossRefGoogle Scholar
  24. 24.
    Gruen D, Robinson T and Stone A (2005). Output gaps in real time: how reliable are they. Econ Rec 81: 6–18 CrossRefGoogle Scholar
  25. 25.
    Hamilton JD (1994). Time series analysis. Princeton University Press, Princeton Google Scholar
  26. 26.
    Harvey AC (1985). Trends and cycles in macroeconomic time series. J Bus Econ Stat 3: 216–227 CrossRefGoogle Scholar
  27. 27.
    Johansen PR, Eika T (2000) Drivkrefter bak konjunkturforløpet på 1990-tallet (driving forces behind cyclical developments in the 1990s). Economic Survey 6/2000, Statistics NorwayGoogle Scholar
  28. 28.
    Kydland FE, Prescott EC (1990) Business cycles: real facts and a monetary myth. Federal Reserve Bank of Minneapolis Quarterly Review. Spring, pp 3–18Google Scholar
  29. 29.
    Leamer EE (1978). Regression selection strategies and revealed priors. J Am Stat Assoc 73: 580–587 CrossRefGoogle Scholar
  30. 30.
    Leitemo K and Lønning I (2006). Simple monetary policymaking without the output gap. J Money Edit Bank 38: 1619–1640 CrossRefGoogle Scholar
  31. 31.
    McCallum BT (1998). Issues in the design of monetary policy rules. In: Taylor, J and Woodford, M (eds) Handbook of macroeconomics, pp 1483–1530. North Holland, New York Google Scholar
  32. 32.
    McCallum BT (2001). Should monetary policy respond strongly to output gaps?. Am Econ Rev 91: 258–262 CrossRefGoogle Scholar
  33. 33.
    McDermott CJ, Scott A (2000) Concordance in business cycles. IMF working Paper 00/37Google Scholar
  34. 34.
    Ministry of Finance (1997) Fakta og analyser (facts and analyses). Annex to Report No. 4 (1996–1997) to the Storting, Long-term programme 1998–2001, 74Google Scholar
  35. 35.
    Nelson CR and Plosser CI (1982). Trends and random walks in macroeconomic time series. J Monet Econ 10: 129–162 CrossRefGoogle Scholar
  36. 36.
    Okun AM (1962) Potential GNP: its measurement and significance. In: Proceedings of the business and economic statistics section of the American Statistical Association, pp 98–103Google Scholar
  37. 37.
    Olsen K, Qvigstad JF, Røisland Ø (2002) Monetary policy in real time: the role of simple rules, Bis papers no. 19Google Scholar
  38. 38.
    Orphanides A (2001). Monetary policy rules based on real-time data. Am Econ Rev 91: 964–985 CrossRefGoogle Scholar
  39. 39.
    Orphanides A (2003). Monetary policy evaluation with noisy information. J Monet Econ 50: 605–631 CrossRefGoogle Scholar
  40. 40.
    Orphanides A and van Norden S (2002). The unreliability of output gap estimates in real time. Rev Econ Stat 84: 569–583 CrossRefGoogle Scholar
  41. 41.
    Orphanides A and van Norden S (2005). The reliability of inflation forecasts based on output gap estimates in real time. J Money Edit Bank 37: 583–601 CrossRefGoogle Scholar
  42. 42.
    Orphanides A, Porter RD, Reifschneider D, Tetlow R and Finan F (2000). Errors in the measurement of the output gap and the design of monetary policy. J Econ Bus 52: 117–141 CrossRefGoogle Scholar
  43. 43.
    Rotenberg JJ and Woodford M (1997). An optimization-based econometric model for the evaluation of monetary policy. NBER Macroecon Ann 12: 297–346 CrossRefGoogle Scholar
  44. 44.
    Rudebusch GD (2002). Assessing nominal income rules for monetary policy with model and data uncertainty. Econ J 112: 402–432 CrossRefGoogle Scholar
  45. 45.
    Rünstler G (2002) The information content of real-time output gap estimates: an application to the Euro area. European Central Bank Working Paper No. 182Google Scholar
  46. 46.
    Scott A (2000) Stylised facts from output gap measures. Reserve Bank of New Zealand Discussion Paper, DP2000/07Google Scholar
  47. 47.
    Smets F (2002). Output gap uncertainty: does it matter for the Taylor rule. Empir Econ 22: 113–129 CrossRefGoogle Scholar
  48. 48.
    Spencer DE (2004) Output gap uncertainty and monetary policy during the 1970s. Topics in macroeconomics, 4. Article 2Google Scholar
  49. 49.
    Stock JH, Watson MW (1998) Business cycle fluctuations in US macroeconomic time series. Working Paper No. 6528. National Bureau of Economic ResearchGoogle Scholar
  50. 50.
    Stock JH and Watson MW (2003). Forecasting output and inflation: the role of asset prices. J Econ Lit XLI: 788–829 CrossRefGoogle Scholar
  51. 51.
    Stock JH and Watson MW (2004). Combination forecasts of output growth in a seven-country data set. J Forecast 23: 405–430 CrossRefGoogle Scholar
  52. 52.
    Svensson LEO (1997). Inflation forecast targeting: implementing and monitoring inflation targets. Eur Econ Rev 41: 1111–1146 CrossRefGoogle Scholar
  53. 53.
    Svensson LEO (2000). Open-economy inflation targeting. J Int Econ 50: 155–183 CrossRefGoogle Scholar
  54. 54.
    Svensson LEO and Woodford M (2003). Indicator variables for optimal policy. J Monet Econ 50: 691–720 CrossRefGoogle Scholar
  55. 55.
    Svensson LEO and Woodford M (2005). Implementing optimal policy through inflation-forecast targeting. In: Bernanke, BS and Woodford, M (eds) The inflation-targeting debate, pp 19–83. University of Chicago Press, Chicago Google Scholar
  56. 56.
    West KD (1996). Asymptotic inference about predictive ability. Econometrica 64: 1067–1084 CrossRefGoogle Scholar
  57. 57.
    Wright JH (2003) Forecasting US inflation by Bayesian Model Averaging. International Finance Discussion Paper 780, Board of Governors of the Federal Reserve SystemGoogle Scholar

Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Hilde C. Bjørnland
    • 1
    • 2
    Email author
  • Leif Brubakk
    • 2
  • Anne Sofie Jore
    • 2
  1. 1.Department of EconomicsNorwegian School of Management (BI)OsloNorway
  2. 2.Norges BankOsloNorway

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