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An Inquiry Into Triangle Recursive Grouping Data Barycenter Forecasting Method And Application

机译:三角递归分组数据重心预测方法的研究与应用

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A new and useful parameter estimating method for dynamic econometric model and an novel forecasting method are proposed in this paper.These methods could deal with the fitting and forecasting of economy dynamic model and could greatly decrease the forecasting errors result from the singularity of the real data.Moreover, the strict hypothetical conditions in least squares method can be released in the method presented in this paper, which overcome the shortcomings of least squares method and expanded the application of data barycentre method.The new methods are applied to Chinese steel consumption forecasting based on the historic data.It is shown that the result of fitting and forecasting was satisfactory.From the comparison between the new forecasting method and the least squares method, we conclude that the fitting and forecasting results using data barycentre method are more stable than that using least squares regression forecasting method, and the computation of data barycentre forecasting method is simpler than that of least squares method.As a result, the data barycentre method is convenient to use in technical economy.
机译:本文提出了一种新的有用的动态计量经济学模型参数估计方法和一种新颖的预测方法,这些方法既可以处理经济动态模型的拟合和预测,又可以大大减少实际数据奇异性导致的预测误差此外,本文提出的方法可以释放严格的最小二乘假设条件,克服了最小二乘方法的缺点,扩大了数据重心法的应用范围。通过对新的预测方法和最小二乘方法的比较,可以得出结论:采用数据重心法比采用最小二乘法拟合和预测结果更稳定。最小二乘回归预测方法及其计算数据重心预测方法比最小二乘方法更简单。因此,数据重心预测方法在技术经济中使用方便。

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