Hostname: page-component-cd9895bd7-jn8rn Total loading time: 0 Render date: 2024-12-23T06:10:48.287Z Has data issue: false hasContentIssue false
  • English
  • Français

TREND-CYCLE DECOMPOSITION OF OUTPUT AND EURO AREA INFLATION FORECASTS: A REAL-TIME APPROACH BASED ON MODEL COMBINATION

Published online by Cambridge University Press:  09 October 2013

Pierre Guérin ,
Laurent Maurin  and
Matthias Mohr
Pierre Guérin*
Affiliation:
Bank of Canada
Laurent Maurin
Affiliation:
European Central Bank
Matthias Mohr
Affiliation:
European Central Bank
*
Address correspondence to: Pierre Guérin, Bank of Canada, 234 Wellington Street, Ottawa, ON K1A 0G9, Canada; e-mail: [email protected].
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper estimates univariate and multivariate trend-cycle decomposition models of GDP and considers the novel possibility of regime switches in the growth of potential output. We compute both ex post and real-time estimates of the output gap to check the stability of our estimates to GDP data revisions. We find some evidence of regime changes in the growth of potential output during the recessions experienced by the euro area. We also run a forecasting experiment to evaluate the predictive power of the output gap for inflation. The benchmark autoregressive model tends to obtain the best forecasts for one-quarter-ahead forecasts, but the output gap measures help to forecast inflation for longer horizons.

Keywords

Unobserved Components ModelPhillips CurveMarkov SwitchingInflation Forecast
Type
Articles
Information
Macroeconomic Dynamics , Volume 19 , Issue 2 , March 2015 , pp. 363 - 393
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Atkeson, A. and Ohanian, L. (2001) Are Phillips curves useful for forecasting inflation? Federal Reserve Bank of Minneapolis Quarterly Review 25 (1), 2–11.Google Scholar
Baxter, M. and King, R.G. (1999) Measuring business cycles: Approximate band-pass filters for economic time series. Review of Economics and Statistics 81 (4), 575–593.Google Scholar
Beveridge, R. and Nelson, C. (1981) A new approach to decomposition of economic time series into permanent and transitory components with particular attention to measurement of the business cycle. Journal of Monetary Economics 7 (2), 151–174.CrossRefGoogle Scholar
Clark, P. (1987) The cyclical component of US economic activity. Quarterly Journal of Economics 102 (1), 797–814.Google Scholar
Clark, T. and McCracken, M. (2005) Evaluating direct multistep forecasts. Econometric Reviews 24 (4), 369–404.CrossRefGoogle Scholar
Clark, T. and McCracken, M. (2009a) Nested Forecast Model Comparisons: A New Approach to Testing Equal Accuracy. Working paper 50, Federal Reserve Bank of St. Louis.Google Scholar
Clark, T. and McCracken, M. (2009b) Tests of equal predictive ability with real-time data. Journal of Business and Economic Statistics 27 (4), 441–454.Google Scholar
Cogley, T. and Sargent, T. (2005). Drifts and volatilities: Monetary policies and outcomes in the post WWII US. Review of Economic Dynamics 8 (2), 262–302.Google Scholar
Diebold, F.X. and Mariano, R.S. (1995) Comparing predictive accuracy. Journal of Business and Economic Statistics 13 (3), 253–263.Google Scholar
Drechsel, K. and Maurin, L. (2011) Flow on conjunctural information and forecast of euro area economic activity. Journal of Forecasting 30 (3), 336–354.Google Scholar
Fernández-Villaverde, J., Guerrón-Quintana, P., and Rubio-Ramirez, J. (2010) Fortune or Virtue: Time-Variant Volatilities versus Parameter Drifting in US data. NBER Working Paper 15928.Google Scholar
Gali, J. and Gertler, M. (1999) Inflation dynamics: a structural econometric analysis. Journal of Monetary Economics 44 (2), 195–222.CrossRefGoogle Scholar
Gali, J., Gertler, M., and Lopez-Salido, J.D. (2001) European inflation dynamics. European Economic Review 45 (7), 1237–1270.CrossRefGoogle Scholar
Garratt, A., Lee, K., Mise, E., and Shields, K. (2008) Real-time representations of the output gap. Review of Economics and Statistics 90 (4), 792–804.CrossRefGoogle Scholar
Gordon, R.J. (1997). The time-varying NAIRU and its implications for economic policy. Journal of Economic Perspectives 11 (1), 11–32.Google Scholar
Hamilton, J. (1989) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57 (2), 357–384.Google Scholar
Hamilton, J. (1994) Time Series Analysis. Princeton, NJ: Princeton University Press.Google Scholar
Harvey, D., Leybourne, S., and Newbold, P. (1997) Testing the equality of prediction mean squared errors. International Journal of Forecasting 13 (2), 281–291.Google Scholar
Hodrick, R.J. and Prescott, E. (1997) Postwar U.S. business cycles: An empirical investigation. Journal of Money, Credit and Banking 29 (1), 1–16.Google Scholar
Hondroyiannis, G., Swamy, P., and Tavlas, G. (2009) The new Keynesian Phillips curve in a time-varying coefficient environment: Some European evidence. Macroeconomic Dynamics 13 (2), 149–166.Google Scholar
Kim, C. and Nelson, C. (1999a) Friedman's plucking model of business fluctuations: Tests and estimates of permanent and transitory components. Journal of Money, Credit and Banking 31 (3), 317–334.Google Scholar
Kim, C. and Nelson, C. (1999b) State-Space Models with Regime Switching: Classical and Gibbs-Sampling Approaches with Applications. Cambridge, MA: MIT Press.Google Scholar
Kuttner, K. (1994) Estimating potential output as a latent variable. Journal of Business and Economic Statistics 12 (3), 361–368.Google Scholar
Lanne, M., Luetkepohl, H., and Maciejowska, K. (2010) Structural vector autoregressions with Markov switching. Journal of Economic Dynamics and Control 34 (2), 121–131.Google Scholar
Luetkepohl, H. (2005) New Introduction to Multiple Time Series Analysis. Berlin: Springer.CrossRefGoogle Scholar
Marcellino, M. and Musso, A. (2011) The reliability of real-time estimates of the euro area output gap. Economic Modelling 28 (4), 1842–1856.Google Scholar
Morley, J., Nelson, C., and Zivot, E. (2003) Why are the Beveridge–Nelson and unobserved-components decompositions of GDP so different? Review of Economics and Statistics 85 (2), 235–243.Google Scholar
Morley, J. and Piger, J. (2012) The asymmetric business cycle. Review of Economics and Statistics 94 (1), 208–221.Google Scholar
Orphanides, A. and Van Norden, S. (2002) The unreliability of output-gap estimates in real time. Review of Economics and Statistics 84 (4), 569–583.Google Scholar
Orphanides, A. and van Norden, S. (2005) The reliability of inflation forecasts based on output gap estimates in real time. Journal of Money, Credit and Banking 37 (3), 583–601.Google Scholar
Perron, P. and Wada, T. (2009) Let's take a break: Trends and cycles in US real GDP. Journal of Monetary Economics 56 (6), 749–765.Google Scholar
Proietti, T., Musso, A., and Westermann, T. (2007) Estimating potential output and the output gap for the euro area: A model-based production function approach. Empirical Economics 33 (1), 85–113.Google Scholar
Rigobon, R. (2003) Identification through heteroskedasticity. Review of Economics and Statistics 85 (4), 777–792.CrossRefGoogle Scholar
Rudd, J. and Whelan, K. (2007) Modeling inflation dynamics: A critical review of recent research. Journal of Money, Credit and Banking 39 (s1), 155–170.Google Scholar
Sims, C. and Zha, T. (2006) Were there regime switches in US monetary policy? American Economic Review 96 (1), 54–81.Google Scholar
Sinclair, T. (2009) Asymmetry in the business cycle: Friedman's plucking model with correlated innovations. Studies in Nonlinear Dynamics and Econometrics 14 (1), 1–31.Google Scholar
Stock, J.H. and Watson, M.W. (2008) Phillips Curve Inflation Forecasts. Working paper 14322, NBER.Google Scholar
Timmermann, A. (2006). Forecast Combinations, Handbook of Economic Forecasting, Vol. 1, Chap. 4, pp. 135–196. Amsterdam: Elsevier.Google Scholar
Watson, M. (1986) Univariate detrending methods with stochastic trends. Journal of Monetary Economics 18 (1), 49–75.Google Scholar