Lag Augmentation in Dynamic Econometric Models

众所周知，在具有非平稳回归量的动态模型中，参数的Wald检验统计量有时具有非标准的渐近分布。为了解决这一问题，提出了一种基于滞后增强的动态模型估计方法。与其他先进但复杂的方法相比，它是非常有用的，因为LA方法产生具有渐近卡方分布的Wald统计量。1998年，我们证明了LA方法适用于通常的动态回归模型，尽管该方法最初仅用于向量自回归（VAR）模型。此外，我们发现Wald检验统计量倾向于具有较长滞后长度的滞后变量而不是一个滞后变量具有更高的功率。1999年，在综合实验的基础上，我们提出了选择合适滞后长度的标准，该标准基于系数参数估计方差-协方差矩阵的广义方差。进一步，我们推广了LA方法在回归量和误差项相关的联立方程组中的应用。因此，已经表明LA与工具变量（IV）方法一起适用。蒙特卡洛实验证明了本文所提方法的有效性。

It has been widely known that the Wald test statistic for parameters in a dynamic model with non-stationary regressors occasionally has a non-standard asymptotic distribution. To cope with this difficulty, the lag augmentation (LA) approach has been proposed for estimating a dynamic model. It is known to be quite useful compared with other advanced but complicated methods, since the LA approach produces the Wald statistic with asymptotically chi-square distribution.In 1998, we showed that the LA approach is applicable to usual dynamic regression models, although the method was originally developed solely for vector autoregressive (VAR) models. Further, we found that the Wald test statistic tends to have higher power with a lagged variable of a longer lag length rather than that of one lag. In 1999, based upon the comprehensive experiments, we have proposed the criteria for selecting a suitable lag length, which is based upon a generalized variance of an estimated variance-covariance matrix of coefficient parameters. Further, we have generalized the application of the LA approach to simultaneous equation systems where regressors and error terms are correlated. Thus, it has been shown that the LA is applicable in conjunction with the instrumental variable (IV) method. The Monte Carlo experiment has revealed the effectiveness of approaches proposed in the present research.