论文标题
高维混合频率IV回归
High-dimensional mixed-frequency IV regression
论文作者
论文摘要
本文引入了以混合频率采样的数据进行的高维线性IV回归。我们表明，可以在低频仪器变量上识别并准确地估计高频协变量的高维斜率参数。该模型的显着特征是它允许递给高维数据集而不施加近似稀疏性限制。我们提出了一个由Tikhonov调度的估计器，并得出了其均值整合平方误差的收敛速率，以用于时间序列数据。估计器具有闭合形式的表达，易于计算，并且在我们的蒙特卡洛实验中表现出了出色的性能。我们估计澳大利亚电力现货市场上供应的实时价格弹性。我们的估计表明，供应是相对非弹性的，并且整天的弹性是异质的。
This paper introduces a high-dimensional linear IV regression for the data sampled at mixed frequencies. We show that the high-dimensional slope parameter of a high-frequency covariate can be identified and accurately estimated leveraging on a low-frequency instrumental variable. The distinguishing feature of the model is that it allows handing high-dimensional datasets without imposing the approximate sparsity restrictions. We propose a Tikhonov-regularized estimator and derive the convergence rate of its mean-integrated squared error for time series data. The estimator has a closed-form expression that is easy to compute and demonstrates excellent performance in our Monte Carlo experiments. We estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic and that its elasticity is heterogeneous throughout the day.
