We propose moving average threshold heterogeneous autoregressive (MAT‐HAR) models as a novel combination of heterogeneous autoregression (HAR) and threshold autoregression (TAR). The MAT‐HAR has multiple groups of lags of a target series, and a threshold term can appear in each group. The threshold is a moving average of lagged target series, which guarantees time‐varying thresholds and simple estimation via least squares. We show via Monte Carlo simulations that the MAT‐HAR has sharp in‐sample and out‐of‐sample performance. An empirical application on the industrial production of Japan suggests that significant threshold effects exist, and the MAT‐HAR has a higher forecast accuracy than the HAR.

中文翻译：

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移动平均阈值异质自回归（MAT‐HAR）模型

我们提出移动平均阈值异质自回归（MAT‐HAR）模型作为异质自回归（HAR）和阈值自回归（TAR）的新颖组合。MAT-HAR具有目标序列的多组滞后，并且每组中都可以出现一个阈值项。该阈值是滞后目标序列的移动平均值，可确保随时间变化的阈值和通过最小二乘法的简单估算。通过蒙特卡洛仿真，我们证明MAT‐HAR具有出色的样本内和样本外性能。在日本工业生产上的经验应用表明，存在明显的阈值效应，并且MAT-HAR的预测准确性高于HAR。