A Comparison of Alternative Instrumental Variables Estimators of a Dynamic Linear Model
比较了动态线性模型中两种常用工具变量估计量，发现一种渐近最优估计量在特定数据生成过程中效率更高，模拟表明渐近理论在典型样本量下表现良好但检验统计量有时存在尺寸扭曲。
Using a dynamic linear equation that has a conditionally homoskedastic moving average disturbance, we compare two parameterizations of a commonly used instrumental variables estimator (Hansen (1982)) to one that is asymptotically optimal in a class of estimators that includes the conventional one. (Hansen (1985)). We find that for some plausible data generating processes, the optimal one is distinctly more efficient asymptotically. simulations indicate that in samples of size typically available, asymptotic theory describes the distribution of the parameter estimates reasonably well, but that test statistics sometimes are poorly sized.