Forecasting Commodity Prices with Mixed-Frequency Data: An OLS-Based Generalized ADL Approach

Yu‐chin Chen, Wen-Jen Tsay
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引用次数: 9

Abstract

This paper presents a generalized autoregressive distributed lag (GADL) model for conducting regression estimations that involve mixed-frequency data. As an example, we show that daily asset market information - currency and equity mar- ket movements - can produce forecasts of quarterly commodity price changes that are superior to those in the previous research. Following the traditional ADL lit- erature, our estimation strategy relies on a Vandermonde matrix to parameterize the weighting functions for higher-frequency observations. Accordingly, infer- ences can be obtained using ordinary least squares principles without Kalman fi ltering, non-linear optimizations, or additional restrictions on the parameters. Our fi ndings provide an easy-to-use method for conducting mixed data-sampling analysis as well as for forecasting world commodity price movements.
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用混合频率数据预测商品价格:基于ols的广义ADL方法
本文提出了一种广义自回归分布滞后(GADL)模型,用于混合频率数据的回归估计。作为一个例子,我们展示了每日的资产市场信息——货币和股票市场的运动——可以产生季度商品价格变化的预测,比以前的研究要好。根据传统的ADL文献,我们的估计策略依赖于Vandermonde矩阵来参数化高频观测的权重函数。因此,可以使用普通最小二乘原理获得推论,而不需要卡尔曼滤波、非线性优化或对参数的附加限制。我们的研究结果为进行混合数据抽样分析以及预测世界商品价格走势提供了一种易于使用的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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