Nonparametric Time Varying IV-SVARs: Estimation and Inference
研究了用外部工具变量识别的结构向量自回归模型中时变脉冲响应函数的估计与推断方法，基于核估计器允许非参数时变，计算简单且能处理弱工具变量，模拟显示小样本下也有良好覆盖。
Abstract This paper studies the estimation and inference of time-varying impulse response functions in structural vector autoregressions (SVARs) identified with external instruments. Building on kernel estimators that allow for nonparametric time variation, we derive the asymptotic distributions of the relevant quantities. Our estimators are simple and computationally trivial and allow for potentially weak instruments. Simulations suggest satisfactory empirical coverage even in relatively small samples as long as the underlying parameter instabilities are sufficiently smooth. We illustrate the methods by studying the time-varying effects of global oil supply news shocks on US industrial production.