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与一类渐近最优估计量的效率，发现最优估计量在部分数据生成过程中渐近效率更高，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。
Abstract Using a dynamic linear equation that has a conditionally homoscedastic moving average disturbance, we compare two parameterizations of a commonly used instrumental variables estimator to one that is asymptotically optimal in a class of estimators that includes the conventional one. 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. KEY WORDS: Asymptotic approximationEfficient estimationOptimal estimationSimulationTest statistics