Lahiri Kajal, Peng Huaming, Zhao Yongchen
Department of Economics, University at Albany, SUNY, Albany, New York, 12222, USA.
Int J Forecast. 2015 Jan;31(1):113-129. doi: 10.1016/j.ijforecast.2014.03.005.
We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value", as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the probability forecasts. The proposed test does not require the probabilities to be converted to binary forecasts before testing, and it accommodates serial correlation and skewness in the forecasts. We find that the number of forecasters making valuable forecasts decreases sharply as the horizon increases. The beta-transformed linear pool combination scheme, based on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures, including calibration and sharpness. The test helps to identify the good forecasters ex ante, and therefore contributes to the accuracy of the combined forecasts.
我们结合了美国专业预测者调查中实际国内生产总值下降的概率预测，剔除了那些根据柯伊伯技能得分以及默顿（1981）意义上没有“价值”的预测。为此，我们使用一个简单的检验来评估概率预测。所提出的检验在检验前不需要将概率转换为二元预测，并且它考虑了预测中的序列相关性和偏度。我们发现，随着预测期限的增加，做出有价值预测的预测者数量急剧减少。基于有价值的个体预测的贝塔变换线性池组合方案，在包括校准和锐度在内的一些绩效指标上，对于所有期限都表现优于简单平均值。该检验有助于事前识别优秀的预测者，从而提高组合预测的准确性。
