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Real time representation of the UK output gap in the presence of model uncertainty

Author

Listed:
  • Garratt, Anthony
  • Lee, Kevin
  • Mise, Emi
  • Shields, Kalvinder

Abstract

We undertake an empirical analysis of the UK output gap using real-time data and an approach that accommodates, in a coherent way, three types of uncertainty when measuring the gap. These are model uncertainty (associated with the choice of model and de-trending technique), estimation uncertainty (with a given model) and measurement uncertainty (associated with the reliability of the data). The approach employs VAR models, along with Bayesian-style 'model averaging' procedures, to jointly explain and forecast real-time measures and realisations of output series. A comprehensive representation of the UK output gap and the associated uncertainties are provided in real time by probability forecasts over 1961q2-2005q4.

Suggested Citation

  • Garratt, Anthony & Lee, Kevin & Mise, Emi & Shields, Kalvinder, 2009. "Real time representation of the UK output gap in the presence of model uncertainty," International Journal of Forecasting, Elsevier, vol. 25(1), pages 81-102.
  • Handle: RePEc:eee:intfor:v:25:y:2009:i:1:p:81-102
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    2. Kevin Lee & James Morley & Kalvinder Shields, 2015. "The Meta Taylor Rule," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 47(1), pages 73-98, February.
    3. Garratt, Anthony & Mitchell, James & Vahey, Shaun P., 2014. "Measuring output gap nowcast uncertainty," International Journal of Forecasting, Elsevier, vol. 30(2), pages 268-279.
    4. Michael P. Clements & Ana Beatriz Galvão, 2011. "Improving Real-time Estimates of Output Gaps and Inflation Trends with Multiple-vintage Models," Working Papers 678, Queen Mary University of London, School of Economics and Finance.
    5. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    6. Clements, Michael P. & Galvão, Ana Beatriz, 2013. "Forecasting with vector autoregressive models of data vintages: US output growth and inflation," International Journal of Forecasting, Elsevier, vol. 29(4), pages 698-714.
    7. Garratt, Anthony & Lee, Kevin & Shields, Kalvinder, 2016. "Forecasting global recessions in a GVAR model of actual and expected output," International Journal of Forecasting, Elsevier, vol. 32(2), pages 374-390.
    8. Bell, William Paul, 2009. "Adaptive interactive expectations: dynamically modelling profit expectations," MPRA Paper 38260, University Library of Munich, Germany, revised 09 Feb 2010.
    9. repec:taf:jnlbes:v:35:y:2017:i:3:p:420-433 is not listed on IDEAS
    10. Kevin Lee & Nilss Olekalns & Kalvinder Shields, 2013. "Meta Taylor Rules for the UK and Australia; Accommodating Regime Uncertainty in Monetary Policy Analysis Using Model Averaging Methods," Manchester School, University of Manchester, vol. 81, pages 28-53, October.
    11. Anthony Garratt & Kevin Lee & Kalvinder Shields, 2014. "Forecasting Global Recessions in a GVAR Model of Actual and Expected Output in the G7," Discussion Papers 2014/06, University of Nottingham, Centre for Finance, Credit and Macroeconomics (CFCM).
    12. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    13. Anh Dinh Minh Nguyen, 2017. "U.K. Monetary Policy under Inflation Targeting," Bank of Lithuania Working Paper Series 41, Bank of Lithuania.

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