Alternative Procedures for Estimating Vector Autoregressions Identified with Long-Run Restrictions
指出标准方法使用特定谱密度估计量，提出替代方法，并通过蒙特卡洛实验（数据来自真实商业周期模型）证明替代方法在小样本下偏差更小、均方误差更小、置信区间覆盖率更好。
We show that the standard procedure for estimating long-run identified vector autoregressions uses a particular estimator of the zero-frequency spectral density matrix of the data.We develop alternatives to the standard procedure and evaluate the properties of these alternative procedures using Monte Carlo experiments in which data are generated from estimated real business cycle models.We focus on the properties of estimated impulse response functions.In our examples, the alternative procedures have better small sample properties than the standard procedure, with smaller bias, smaller mean square error and better coverage rates for estimated confidence intervals.