A comparison of autoregressive distributed lag and dynamic OLS cointegration estimators in the case of a serially correlated cointegration error
协整误差序列相关时ADL与动态OLS协整估计量的比较AI 解读 ↓
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协整误差序列相关时ADL与动态OLS协整估计量的比较
- 研究问题
- 协整误差存在序列相关时，ADL与不含因变量滞后项的增广静态模型所对应的DOLS，在估计精度和推断可靠性上如何比较？
- 方法与思路
- 作者在误差服从VAR(1)的三角数据生成过程中进行蒙特卡洛比较，并考察模型阶数选择、更广估计量及其他误差过程。
- 材料陈述的贡献
- 摘要称，所考察设定中ADL表现优于DOLS，并将差异归因于DOLS残留的截断偏误；增加相关滞后项可缩小二者差距。
- 阅读关注点
- 核对截断偏误的定义与推导、各估计量的滞后设置和比较标准，以及信息准则选阶与其他误差过程下结果的适用范围。
- 阅读边界
- 仅据所给出版社摘要，无法核验模拟参数、样本规模及最优性判据；摘要中的优势结论不能推广为所有协整模型下的排序。
原文摘要
This paper deals with a family of parametric, single-equation cointegration estimators that arise in the context of the autoregressive distributed lag (ADL) models. We particularly focus on a subclass of the ADL models, those that do not involve lagged values of the dependent variable, referred to as augmented static (AS) models. The general ADL and the restricted AS models give rise to the ADL and dynamic OLS (DOLS) estimators, respectively. The relative performance of these estimators is assessed by means of Monte Carlo simulations in the context of a triangular data generation process (DGP) where the cointegration error and the error that drives the regressor follow a VAR(1) process. The results suggest that ADL fares consistently better than DOLS, both in terms of estimation precision and reliability of statistical inferences. This is due to the fact that DOLS, as opposed to ADL, does not fully correct for the second-order asymptotic bias effects of cointegration, since a "truncation bias" always remains. As a result, the performance of DOLS approaches that of ADL, as the number of lagged values of the first difference of the regressor in the AS model increases. Another set of Monte Carlo simulations suggests that the commonly used information criteria select the correct order of the ADL model quite frequently, thus making the employment of ADL over DOLS quite appealing and feasible. Additional results suggest that ADL re-emerges as the optimal estimator within a wider class of asymptotically efficient estimators including, apart from DOLS, the semiparametric fully modified least squares (FMLS) estimator of Phillips and Hansen (1990, Review of Economic Studies 57, 99--125), the non-linear parametric estimator (PL) of Phillips and Loretan (1991, Review of Economic Studies 58, 407--36) and the system-based maximum likelihood estimator (JOH) of Johansen (1991, Econometrica 59, 1551--80). All the aforementioned results are robust to alternative models for the error term, such as vector autoregressions of higher order, or vector moving average processes. Copyright Royal Economic Socciety 2004