Forecasting with Universal Approximators and a Learning Algorithm-使用通用逼近器和学...

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英文文献：Forecasting with Universal Approximators and a Learning Algorithm-使用通用逼近器和学习算法进行预测
英文文献作者：Anders Bredahl Kock
英文文献摘要：
This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the Kolmogorov-Gabor polynomials, as well as the Elliptic Basis Function Networks. Even though forecast combination has a long history in econometrics focus has not been on proving loss bounds for the combination rules applied. We apply the Weighted Average Algorithm (WAA) of Kivinen and Warmuth (1999) for which such loss bounds exist. Specifically, one can bound the worst case performance of the WAA compared to the performance of the best single model in the set of models combined from. The use of universal approximators along with a combination scheme for which explicit loss bounds exist should give a solid theoretical foundation to the way the forecasts are performed. The practical performance will be investigated by considering various monthly postwar macroeconomic data sets for the G7 as well as the Scandinavian countries.

本文采用三种通用逼近法进行预测。它们是人工神经网络，Kolmogorov-Gabor多项式，以及椭圆基函数网络。尽管预测组合在计量经济学中有着悠久的历史，但研究的重点并不是证明组合规则的损失界限。我们采用Kivinen和Warmuth(1999)的加权平均算法(WAA)，该算法存在这样的损失边界。具体地说，可以将WAA的最坏情况性能与所组合的模型集中的最佳单一模型的性能进行比较。通用逼近器的使用以及明确损失界限存在的组合方案应该为预测的执行方式提供坚实的理论基础。实际的表现将通过考虑战后七国集团和斯堪的纳维亚国家的各种月度宏观经济数据集来进行调查。

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