Evaluating Combined Non-Replicable Forecasts
Macroeconomic 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.
|Date of creation:||Dec 2010|
|Date of revision:|
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- Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010.
"How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan,"
Working Papers in Economics
10/16, University of Canterbury, Department of Economics and Finance.
- 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.
- Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2010. "How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan," KIER Working Papers 720, Kyoto University, Institute of Economic Research.
- Chia-Lin Chang & Philip Hans Franses & Michael McAleer, 2009. "How Accurate are Government Forecasts of Economic Fundamentals? The Case of Taiwan," CIRJE F-Series CIRJE-F-637, CIRJE, Faculty of Economics, University of Tokyo.
- Philip Hans Franses & Michael McAleer & Rianne Legerstee, 2009.
"Expert opinion versus expertise in forecasting,"
Netherlands Society for Statistics and Operations Research, vol. 63(3), pages 334-346.
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