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Are Forecast Updates Progressive?

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Abstract

Many macro-economic forecasts and forecast updates, such as those from the IMF and OECD, typically involve both a model component, which is replicable, as well as intuition (namely, expert knowledge possessed by a forecaster), which is non-replicable. . Learning from previous mistakes can affect both the replicable component of a model as well as intuition. If learning, and hence forecast updates, are progressive, forecast updates should generally become more accurate as the actual value is approached. Otherwise, learning and forecast updates would be neutral. The paper proposes a methodology to test whether macro-economic 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 two economic fundamentals, namely the inflation rate and real GDP growth rate. The 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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Paper provided by Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales in its series Documentos del Instituto Complutense de Análisis Económico with number 2011-03.

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Length: 24 pages
Date of creation: 2011
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Handle: RePEc:ucm:doicae:1103

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Keywords: Macro-economic forecasts; econometric models; intuition; learning; progressive forecast updates; forecast errors.;

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  1. Todd E. Clark & Michael W. McCracken, 1999. "Tests of equal forecast accuracy and encompassing for nested models," Research Working Paper 99-11, Federal Reserve Bank of Kansas City.
  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, vol. 27(4), pages 1066-1075, October.
  3. 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.
  4. Mcleer, M. & Mckenzie, C.R., 1989. "When Are Two Step Estimators Efficient?," Papers 179, Australian National University - Department of Economics.
  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. 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.
  7. Bunn, Derek W. & Salo, Ahti A., 1996. "Adjustment of forecasts with model consistent expectations," International Journal of Forecasting, Elsevier, vol. 12(1), pages 163-170, March.
  8. Fiebig, Denzil G. & McAleer, Michael & Bartels, Robert, 1992. "Properties of ordinary least squares estimators in regression models with nonspherical disturbances," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 321-334.
  9. Franses, Ph.H.B.F. & McAleer, M.J. & Legerstee, R., 2008. "Expert opinion versus expertise in forecasting," Econometric Institute Research Papers EI 2008-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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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 & Les Oxley & Felix Chan, 2013. "Modelling and Simulation: An Overview," Documentos del Instituto Complutense de Análisis Económico 2013-16, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales.

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