Threshold factor-augmented vector autoregressive models
本文提出阈值因子增强向量自回归模型，扩展传统FAVAR以捕捉阈值效应，并开发了估计方法。实证表明，货币政策冲击在经济衰退期对实际变量的影响显著减弱。
This paper introduces novel threshold factor-augmented vector autoregressive (FAVAR) models which extend the conventional FAVAR model to allow for the threshold effect. We develop economically sensible identification conditions and propose a method for estimating threshold values, latent factors and regime-dependent parameters. We also study the estimation of impulse response functions using external instruments and provide a bootstrap procedure to compute their confidence intervals. Asymptotic theories are established. Monte Carlo experiments show good finite sample performance. In empirical applications, we investigate the performance of a threshold FAVAR which employs industrial production growth to determine boom and recession regimes. It outperforms the alternative models in forecasting, and suggests that the monetary policy shocks identified using orthogonalized monetary policy surprises have significantly weaker effects in recessions, on certain macroeconomic variables especially the real ones.