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首页> 外文期刊>Journal of statistical computation and simulation >Improved autoregressive forecasts in the presence of non-normal errors
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Improved autoregressive forecasts in the presence of non-normal errors

机译：在存在非正常错误的情况下改进了自回归预测

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摘要

This paper is concerned with obtaining more accurate point forecasts in the presence of non-normal errors. Specifically, we apply the residual augmented least-squares (RALS) estimator to autoregressive models to utilize the additional moment restrictions embodied in non-normal errors. Monte Carlo experiments are performed to compare our RALS forecasts to forecasts based on the ordinary least-squares estimator and the least absolute deviations (LAD) estimator. We find that the RALS approach provides superior forecasts when the data are skewed. Compared to the LAD forecast, the RALS forecast has smaller mean squared prediction errors in the baseline case with normal errors.
机译：本文涉及在存在非正态误差的情况下获得更准确的点预测。具体来说，我们将残差增强最小二乘（RALS）估计器应用于自回归模型，以利用非正态误差中体现的附加矩约束。进行了蒙特卡洛实验，将我们的RALS预测与基于普通最小二乘估计器和最小绝对偏差（LAD）估计器的预测进行比较。我们发现，当数据倾斜时，RALS方法可以提供出色的预测。与LAD预测相比，RALS预测在具有正常误差的基线情况下具有较小的均方预测误差。

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