Identification in Structural Vector Autoregressive Models with Structural Changes, with an Application to US Monetary Policy
推导了结构向量自回归模型在误差协方差和结构参数均可随波动率变化时的局部识别条件，并用美国货币政策数据说明该方法能揭示货币政策自1980年代以来更有效稳定经济。
Abstract A growing line of research makes use of structural changes and different volatility regimes found in the data in a constructive manner to improve the identification of structural parameters in structural vector autoregressions (SVARs). A standard assumption made in the literature is that the reduced form unconditional error covariance matrix varies while the structural parameters remain constant. Under this hypothesis, it is possible to identify the SVAR without needing to resort to additional restrictions. With macroeconomic data, the assumption that the transmission mechanism of the shocks does not vary across volatility regimes is debatable. We derive novel necessary and sufficient rank conditions for local identification of SVARs, where both the error covariance matrix and the structural parameters are allowed to change across volatility regimes. Our approach generalizes the existing literature on ‘identification through changes in volatility’ to a broader framework and opens up interesting possibilities for practitioners. An empirical illustration focuses on a small monetary policy SVAR of the US economy and suggests that monetary policy has become more effective at stabilizing the economy since the 1980s.