Nearly Unbiased Estimation of Autoregressive Models for Bounded Near‐Integrated Stochastic Processes*
研究了有界近单位根随机过程中自回归模型的估计偏差，扩展了两种偏差校正方法，蒙特卡洛模拟表明考虑有界性可改进估计，并用G7国家失业率数据演示。
Abstract 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.