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Analysis of a panel of UK macroeconomic forecasts

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  • DAVID I. HARVEY
  • STEPHEN J. LEYBOURNE
  • PAUL NEWBOLD

Abstract

This paper looks at unobserved components models and examines the implied weighting patterns for signal extraction. There are four main themes. The first concerns the implications of correlated disturbances driving the components, especially those cases in which the correlation is perfect. The second is about the way in which ARIMA-based methods for trend extraction relate to those based on unobserved components. The third explores the impact of heteroscedasticity and irregular spacing and shows how setting up models with t -distributed disturbances leads to weighting patterns which are robust to outliers and breaks. Finally, a comparison is made between implied weighting patterns with kernels used in non-parametric trend estimation and equivalent kernels used in spline smoothing. It is demonstrated that with irregularly spaced data, the weighting used by conventional spline smoothing techniques is not the same as that obtained from the time series model based approach.

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Bibliographic Info

Article provided by Royal Economic Society in its journal The Econometrics Journal.

Volume (Year): 4 (2001)
Issue (Month): 1 ()
Pages: S37-S55

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Handle: RePEc:ect:emjrnl:v:4:y:2001:i:1:p:s37-s55

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Postal: Office of the Secretary-General, School of Economics and Finance, University of St. Andrews, St. Andrews, Fife, KY16 9AL, UK
Phone: +44 1334 462479
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Web page: http://www.res.org.uk/
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Related research

Keywords: Cubic splines; Kalman filter and smoother; Kernels; Robustness; Structural time series model; Trend; Wiener–Kolmogorov filter.;

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Cited by:
  1. Jonas Dovern & Johannes Weisser, 2009. "Accuracy, Unbiasedness and Efficiency of Professional Macroeconomic Forecasts: An empirical Comparison for the G7," Jena Economic Research Papers 2009-091, Friedrich-Schiller-University Jena, Max-Planck-Institute of Economics.
  2. Kajal Lahiri & Gultekin Isiklar, 2010. "Estimating International Transmission of Shocks Using GDP Forecasts: India and Its Trading Partners," Discussion Papers 10-06, University at Albany, SUNY, Department of Economics.
  3. Deschamps, Bruno & Ioannidis, Christos, 2013. "Can rational stubbornness explain forecast biases?," Journal of Economic Behavior & Organization, Elsevier, vol. 92(C), pages 141-151.
  4. Jonas Dovern & Ulrich Fritsche & Jiri Slacalek, 2009. "Disagreement among Forecasters in G7 Countries," Macroeconomics and Finance Series 200906, Hamburg University, Department Wirtschaft und Politik.
  5. Isiklar, Gultekin & Lahiri, Kajal, 2007. "How far ahead can we forecast? Evidence from cross-country surveys," International Journal of Forecasting, Elsevier, vol. 23(2), pages 167-187.
  6. Carlos Capistrán & Gabriel López-Moctezuma, 2010. "Forecast Revisions of Mexican Inflation and GDP Growth," Working Papers 2010-11, Banco de México.
  7. Ager, Philipp & Kappler, Marcus & Osterloh, Steffen, 2007. "The Accuracy and Efficiency of the Consensus Forecasts: A Further Application and Extension of the Pooled Approach," ZEW Discussion Papers 07-058, ZEW - Zentrum für Europäische Wirtschaftsforschung / Center for European Economic Research.
  8. Jordi Pons-Novell, 2004. "Behavioural biases among interest rate forecasters?," Applied Economics Letters, Taylor & Francis Journals, vol. 11(5), pages 319-321.
  9. Harvey, David I. & Newbold, Paul, 2003. "The non-normality of some macroeconomic forecast errors," International Journal of Forecasting, Elsevier, vol. 19(4), pages 635-653.
  10. Kajal Lahiri & Gultekin Isiklar & Prakash Loungani, 2006. "How quickly do forecasters incorporate news? Evidence from cross-country surveys," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(6), pages 703-725.
  11. Capistrán, Carlos & López-Moctezuma, Gabriel, 2014. "Forecast revisions of Mexican inflation and GDP growth," International Journal of Forecasting, Elsevier, vol. 30(2), pages 177-191.
  12. Isiklar, Gultekin, 2005. "On aggregation bias in fixed-event forecast efficiency tests," Economics Letters, Elsevier, vol. 89(3), pages 312-316, December.
  13. Jan-Egbert Sturm & Timo Wollmershäuser, 2008. "The Stress of Having a Single Monetary Policy in Europe," CESifo Working Paper Series 2251, CESifo Group Munich.
  14. Lahiri, Kajal & Sheng, Xuguang, 2008. "Evolution of forecast disagreement in a Bayesian learning model," Journal of Econometrics, Elsevier, vol. 144(2), pages 325-340, June.
  15. Stefan Günnel & Karl-Heinz Tödter, 2009. "Does Benford’s Law hold in economic research and forecasting?," Empirica, Springer, vol. 36(3), pages 273-292, August.

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