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引用次数: 0
摘要
本文为广义脉冲响应(GIRs)引入了一种新颖的两阶段估计和推断程序。广义脉冲响应包括 y 的未来结果对滞后值的多视距线性预测模型中的所有系数(Dufour 和 Renault,1998 年),其中包括 Sims 脉冲响应。传统的最小二乘法(LS)与异方差和自相关一致的协方差估计的精确度较低,往往会导致不可靠的有限样本检验,而带宽和核函数的选择又使问题更加复杂。我们的两阶段方法在估计效率和推断稳健性方面超过了 LS 方法。稳健性源于我们提出的协方差矩阵估计,它无需校正多视距投影残差中的序列相关性。我们的方法能适应非平稳数据,并允许投影视距随样本大小而增长。蒙特卡罗模拟证明,我们的两阶段方法优于 LS 方法。我们运用两阶段法研究了 GIRs,实施了多视距格兰杰因果检验,发现经济不确定性对经济活动产生了短期(1-3 个月)和长期(30 个月)的影响。
Simple robust two-stage estimation and inference for generalized impulse responses and multi-horizon causality
This paper introduces a novel two-stage estimation and inference procedure
for generalized impulse responses (GIRs). GIRs encompass all coefficients in a
multi-horizon linear projection model of future outcomes of y on lagged values
(Dufour and Renault, 1998), which include the Sims' impulse response. The
conventional use of Least Squares (LS) with heteroskedasticity- and
autocorrelation-consistent covariance estimation is less precise and often
results in unreliable finite sample tests, further complicated by the selection
of bandwidth and kernel functions. Our two-stage method surpasses the LS
approach in terms of estimation efficiency and inference robustness. The
robustness stems from our proposed covariance matrix estimates, which eliminate
the need to correct for serial correlation in the multi-horizon projection
residuals. Our method accommodates non-stationary data and allows the
projection horizon to grow with sample size. Monte Carlo simulations
demonstrate our two-stage method outperforms the LS method. We apply the
two-stage method to investigate the GIRs, implement multi-horizon Granger
causality test, and find that economic uncertainty exerts both short-run (1-3
months) and long-run (30 months) effects on economic activities.