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Are Forecast Updates Progressive?

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Author Info

  • Chia-Lin Chang

    (National Chung Hsing University Taichung)

  • Philip Hans Franses

    (Erasmus University Rotterdam)

  • Michael McAleer

    (Erasmus University Rotterdam, Complutense University of Madrid, Kyoto University)

Abstract

Many macroeconomic forecasts and forecast updates like those from IMF and OECD typically involve both a model component, which is replicable, as well as intuition, which is non-replicable. Intuition is expert knowledge possessed by a forecaster. If forecast updates are progressive, forecast updates should become more accurate, on average, as the actual value is approached. Otherwise, forecast updates would be neutral. The paper proposes a methodology to test whether macroeconomic forecast updates are progressive, where the interaction between model and intuition is explicitly taken into account. The data set for the empirical analysis is for Taiwan, where we have three decades of quarterly data available of forecasts and their updates of the inflation rate and real GDP growth rate. Our empirical results suggest that the forecast updates for Taiwan are progressive, and that progress can be explained predominantly by improved intuition.

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Bibliographic Info

Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 13-049/III.

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Date of creation: 25 Mar 2013
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Handle: RePEc:dgr:uvatin:20130049

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Web page: http://www.tinbergen.nl

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Keywords: Macroeconomic forecasts; econometric models; intuition; progressive forecast updates; forecast errors;

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References

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  1. Fiebig, Denzil G. & McAleer, Michael & Bartels, Robert, 1992. "Properties of ordinary least squares estimators in regression models with nonspherical disturbances," Journal of Econometrics, Elsevier, Elsevier, vol. 54(1-3), pages 321-334.
  2. Chang, Chia-Lin & Franses, Philip Hans & McAleer, Michael, 2011. "How accurate are government forecasts of economic fundamentals? The case of Taiwan," International Journal of Forecasting, Elsevier, Elsevier, vol. 27(4), pages 1066-1075, October.
  3. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, Elsevier, vol. 105(1), pages 85-110, November.
  4. Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009. "Expert opinion versus expertise in forecasting," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346.
  5. Pagan, Adrian, 1984. "Econometric Issues in the Analysis of Regressions with Generated Regressors," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(1), pages 221-47, February.
  6. Oxley, Les & McAleer, Michael, 1993. " Econometric Issues in Macroeconomic Models with Generated Regressors," Journal of Economic Surveys, Wiley Blackwell, vol. 7(1), pages 1-40.
  7. Mcleer, M. & Mckenzie, C.R., 1989. "When Are Two Step Estimators Efficient?," Papers, Australian National University - Department of Economics 179, Australian National University - Department of Economics.
  8. McAleer, Michael, 1992. "Efficient Estimation: The Rao-Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares," The Economic Record, The Economic Society of Australia, vol. 68(200), pages 65-72, March.
  9. Bunn, Derek W. & Salo, Ahti A., 1996. "Adjustment of forecasts with model consistent expectations," International Journal of Forecasting, Elsevier, Elsevier, vol. 12(1), pages 163-170, March.
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  1. What Have You Been Reading?
    by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2013-06-11 19:47:00
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Cited by:
  1. Michael McAleer & Felix Chan & Les Oxley, 2013. "Modeling and Simulation: An Overview," Working Papers in Economics 13/18, University of Canterbury, Department of Economics and Finance.

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