Large time‐varying parameter VARs: A nonparametric approach
提出一种非参数估计方法处理大型时变参数向量自回归模型，计算高效且能处理大数据集，在预测关键宏观经济变量上优于常数参数基准，并可用于结构分析，如研究油价冲击对美国工业产出的时变影响。
Summary In this paper we introduce a nonparametric estimation method for a large Vector Autoregression (VAR) with time‐varying parameters. The estimators and their asymptotic distributions are available in closed form. This makes the method computationally efficient and capable of handling information sets as large as those typically handled by factor models and Factor Augmented VARs. When applied to the problem of forecasting key macroeconomic variables, the method outperforms constant parameter benchmarks and compares well with large (parametric) Bayesian VARs with time‐varying parameters. The tool can also be used for structural analysis. As an example, we study the time‐varying effects of oil price shocks on sectoral U.S. industrial output. According to our results, the increased role of global demand in shaping oil price fluctuations largely explains the diminished recessionary effects of global energy price increases.