论文标题
颗粒状仪器变量的推论理论
Inferential Theory for Granular Instrumental Variables in High Dimensions
论文作者
论文摘要
颗粒状仪器变量（GIV）方法学利用面板具有因子误差结构来构造工具以估算具有内生性的结构时间序列模型，即使在控制潜在因素之后。我们将GIV方法扩展到几个维度。首先，我们将标识程序扩展到大型$ n $和大型$ t $框架，这取决于$ n $横截面单元的尺寸分布的渐近Herfindahl指数。其次，我们将这些因素和负载视为未知的因素和负载，并表明在考虑结构参数的限制分布时，估计仪器和因子中的采样误差可以忽略不计。第三，我们表明，在我们的估计算法中，高维精度矩阵中的采样误差可以忽略不计。第四，我们用其他构造仪器过度识别结构参数，从而导致效率提高。提供了蒙特卡洛的证据，以支持我们的渐近理论和对全球原油市场的应用导致新的结果。
The Granular Instrumental Variables (GIV) methodology exploits panels with factor error structures to construct instruments to estimate structural time series models with endogeneity even after controlling for latent factors. We extend the GIV methodology in several dimensions. First, we extend the identification procedure to a large $N$ and large $T$ framework, which depends on the asymptotic Herfindahl index of the size distribution of $N$ cross-sectional units. Second, we treat both the factors and loadings as unknown and show that the sampling error in the estimated instrument and factors is negligible when considering the limiting distribution of the structural parameters. Third, we show that the sampling error in the high-dimensional precision matrix is negligible in our estimation algorithm. Fourth, we overidentify the structural parameters with additional constructed instruments, which leads to efficiency gains. Monte Carlo evidence is presented to support our asymptotic theory and application to the global crude oil market leads to new results.
