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Are Forecast Updates Progressive?

Author

Listed:
  • 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

This discussion paper led to a publication in Mathematics and Computers in Simulation (MATCOM) , 2013, 93(c), 9-18. 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.

Suggested Citation

  • Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2013. "Are Forecast Updates Progressive?," Tinbergen Institute Discussion Papers 13-049/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20130049
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    References listed on IDEAS

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    1. Mcleer, M. & Mckenzie, C.R., 1989. "When Are Two Step Estimators Efficient?," Papers 179, Australian National University - Department of Economics.
    2. Clark, Todd E. & McCracken, Michael W., 2001. "Tests of equal forecast accuracy and encompassing for nested models," Journal of Econometrics, Elsevier, vol. 105(1), pages 85-110, November.
    3. 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.
    4. 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.
    5. 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.
    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. 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.
    8. 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 221-247, February.
    9. 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.
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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.

    More about this item

    Keywords

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

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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