| Budget Amount *help |
¥2,100,000 (Direct Cost: ¥2,100,000)
Fiscal Year 1999: ¥700,000 (Direct Cost: ¥700,000)
Fiscal Year 1998: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Research Abstract |
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.
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