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Combining Non-Replicable Forecasts

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Abstract
Macro-economic forecasts are often based on the interaction between econometric models and experts. A forecast that is based only on an econometric model is replicable and may be unbiased, whereas a forecast that is not based only on an econometric model, but also incorporates an expert’s touch, is non-replicable and is typically biased. In this paper we propose a methodology to analyze the qualities of combined non-replicable forecasts. One part of the methodology seeks to retrieve a replicable component from the non-replicable forecasts, and compares this component against the actual data. A second part modifies the estimation routine due to the assumption that the difference between a replicable and a non-replicable forecast involves a measurement error. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the methodological approach.

Suggested Citation

  • Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010. "Combining Non-Replicable Forecasts," Working Papers in Economics 10/35, University of Canterbury, Department of Economics and Finance.
    • Handle: RePEc:cbt:econwp:10/35
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    1. 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, August.
    2. McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
    3. 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.
    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. 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.
    6. 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.
    7. Mcleer, M. & Mckenzie, C.R., 1989. "When Are Two Step Estimators Efficient?," Papers 179, Australian National University - Department of Economics.
    8. Patton, Andrew J. & Timmermann, Allan, 2007. "Testing Forecast Optimality Under Unknown Loss," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1172-1184, December.
    9. repec:bla:ecorec:v:68:y:1992:i:200:p:65-72 is not listed on IDEAS
    10. MICHAEL McALEER, 1992. "Efficient Estimation: The Rao‐Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares," The Economic Record, The Economic Society of Australia, vol. 68(1), pages 65-72, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Combined forecasts; efficient estimation; generated regressors; replicable forecasts; non-replicable forecasts; expert’s intuition;
    All these keywords.

    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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