Focused information criterion for locally misspecified vector autoregressive models
研究了向量自回归模型在局部误设定下的聚焦信息准则和插件平均方法，聚焦于特定感兴趣参数而非整体模型拟合，并应用于脉冲响应分析，蒙特卡洛模拟支持了理论结果。
This paper investigates the focused information criterion and plug-in average for vector autoregressive models with local-to-zero misspecification. These methods have the advantage of focusing on a quantity of interest rather than aiming at overall model fit. Any (sufficiently regular) function of the parameters can be used as a quantity of interest. We determine the asymptotic properties and elaborate on the role of the locally misspecified parameters. In particular, we show that the inability to consistently estimate locally misspecified parameters translates into suboptimal selection and averaging. We apply this framework to impulse response analysis. A Monte Carlo simulation study supports our claims.