Estimation, Inference, and Empirical Analysis for Time-Varying VAR Models
提出一类系数和误差协方差矩阵随时间平滑变化的向量自回归模型，建立渐近性质、脉冲响应分析、滞后阶数选择准则和系数稳定性检验，并通过美国政府支出乘数应用展示其实用性。
Vector autoregressive (VAR) models are widely used in practical studies, for example, forecasting, modeling policy transmission mechanism, and measuring connection of economic agents. To better capture the dynamics, this article introduces a new class of time-varying VAR models in which the coefficients and covariance matrix of the error innovations are allowed to change smoothly over time. Accordingly, we establish a set of asymptotic properties including the impulse response analyses subject to structural VAR identification conditions, an information criterion to select the optimal lag, and a Wald-type test to determine the constant coefficients. Simulation studies are conducted to evaluate the theoretical findings. Finally, we demonstrate the empirical relevance and usefulness of the proposed methods through an application on U.S. government spending multipliers.