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Adaptive learning forecasting, with applications in forecasting agricultural prices

机译：自适应学习预测及其在农产品价格预测中的应用

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We introduce a new forecasting methodology, referred to as adaptive learning forecasting, that allows for both forecast averaging and forecast error learning. We analyze its theoretical properties and demonstrate that it provides a priori MSE improvements under certain conditions. The learning rate based on past forecast errors is shown to be nonlinear. This methodology is of wide applicability and can provide MSE improvements even for the simplest benchmark models. We illustrate the method's application using data on agricultural prices for several agricultural products, as well as on real GDP growth for several of the corresponding countries. The time series of agricultural prices are short and show an irregular cyclicality that can be linked to economic performance and productivity, and we consider a variety of forecasting models, both univariate and bivariate, that are linked to output and productivity. Our results support both the efficacy of the new method and the forecastability of agricultural prices. (C) 2019 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.
机译：我们引入了一种新的预测方法，称为自适应学习预测，该方法可以进行预测平均和预测错误学习。我们分析了其理论特性，并证明了它在某些条件下可提供先验MSE改进。基于过去的预测误差的学习率显示为非线性。该方法具有广泛的适用性，即使对于最简单的基准模型也可以提供MSE改进。我们使用几种农产品的农产品价格数据以及一些相应国家的实际GDP增长数据说明了该方法的应用。农产品价格的时间序列很短，并且显示出与经济绩效和生产率相关的不规则周期性，我们考虑了与产出和生产率相关的各种单变量和双变量预测模型。我们的结果支持新方法的有效性和农产品价格的可预测性。 （C）2019国际预报员学会。由Elsevier B.V.发布。保留所有权利。

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