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How accurate are government forecasts of economic fundamentals? The case of Taiwan

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  • Chang, Chia-Lin
  • Franses, Philip Hans
  • McAleer, Michael

Abstract

A government's ability to forecast key economic fundamentals accurately can affect business confidence, consumer sentiment, and foreign direct investment, among others. A government forecast based on an econometric model is replicable, whereas one that is not fully based on an econometric model is non-replicable. Governments typically provide non-replicable forecasts (or expert forecasts) of economic fundamentals, such as the inflation rate and real GDP growth rate. In this paper, we develop a methodology for evaluating non-replicable forecasts. We argue that in order to do so, one needs to retrieve from the non-replicable forecast its replicable component, and that it is the difference in accuracy between these two that matters. An empirical example to forecast economic fundamentals for Taiwan shows the relevance of the proposed methodological approach. Our main finding is that the undocumented knowledge of the Taiwanese government reduces forecast errors substantially.

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Bibliographic Info

Article provided by Elsevier in its journal International Journal of Forecasting.

Volume (Year): 27 (2011)
Issue (Month): 4 (October)
Pages: 1066-1075

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Handle: RePEc:eee:intfor:v:27:y:2011:i:4:p:1066-1075

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Web page: http://www.elsevier.com/locate/ijforecast

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Keywords: Government forecasts Generated regressors Replicable government forecasts Non-replicable government forecasts Initial forecasts Revised forecasts;

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References

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  1. Allan Timmermann & Andrew Patton, 2004. "Properties of Optimal Forecasts under Asymmetric Loss and Nonlinearity," Working Papers wp04-05, Warwick Business School, Finance Group.
  2. 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.
  3. Todd E. Clark & Michael W. McCracken, 2000. "Tests of Equal Forecast Accuracy and Encompassing for Nested Models," Econometric Society World Congress 2000 Contributed Papers 0319, Econometric Society.
  4. Philip Hans Franses & Rianne Legerstee, 2010. "Do experts' adjustments on model-based SKU-level forecasts improve forecast quality?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(3), pages 331-340.
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  7. Franses, Philip Hans & Legerstee, Rianne, 2009. "Properties of expert adjustments on model-based SKU-level forecasts," International Journal of Forecasting, Elsevier, vol. 25(1), pages 35-47.
  8. Mcleer, M. & Mckenzie, C.R., 1989. "When Are Two Step Estimators Efficient?," Papers 179, Australian National University - Department of Economics.
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  12. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
  13. Fildes, Robert & Goodwin, Paul & Lawrence, Michael & Nikolopoulos, Konstantinos, 2009. "Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning," International Journal of Forecasting, Elsevier, vol. 25(1), pages 3-23.
  14. 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.
  15. 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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