The paper investigates the estimation bias of autoregressive models for bounded near‐integrated stochastic processes and the performance of the standard procedures in the literature that aim to correct the estimation bias. In some cases, the bounded nature of the stochastic processes worsens the estimation bias effect. The paper extends two popular autoregressive estimation bias correction procedures to cover bounded stochastic processes. Monte Carlo simulations reveal that accounting for the bounded nature of the stochastic processes leads to improvements in the estimation of autoregressive models. Finally, an illustration is given using the unemployment rate of the G7 countries.

中文翻译：

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有界近积分随机过程的自回归模型的几乎无偏估计

本文研究了有界近整合随机过程的自回归模型的估计偏差以及旨在纠正估计偏差的文献中标准程序的性能。在某些情况下，随机过程的有限性质会使估计偏差效应恶化。本文扩展了两种流行的自回归估计偏差校正程序，以涵盖有界随机过程。蒙特卡洛模拟显示，考虑随机过程的有界性质，可以改善自回归模型的估计。最后，给出了使用七国集团国家失业率的例证。