General‐to‐Specific Model Selection Procedures for Structural Vector Autoregressions*
提出从一般到特殊的模型选择方法，用于精简结构向量自回归模型参数，减少估计不确定性。蒙特卡洛实验表明该方法能生成更精确的脉冲响应，并应用于美国货币政策分析。
Abstract Structural vector autoregressive (SVAR) models have emerged as a dominant research strategy in empirical macroeconomics, but suffer from the large number of parameters employed and the resulting estimation uncertainty associated with their impulse responses. In this paper, we propose general‐to‐specific ( Gets ) model selection procedures to overcome these limitations. It is shown that single‐equation procedures are generally efficient for the reduction of recursive SVAR models. The small‐sample properties of the proposed reduction procedure (as implemented using PcGets ) are evaluated in a realistic Monte Carlo experiment. The impulse responses generated by the selected SVAR are found to be more precise and accurate than those of the unrestricted VAR. The proposed reduction strategy is then applied to the US monetary system considered by Christiano, Eichenbaum and Evans ( Review of Economics and Statistics , Vol. 78, pp. 16–34, 1996) . The results are consistent with the Monte Carlo and question the validity of the impulse responses generated by the full system.