高维混合频率IV回归
High-dimensional mixed-frequency IV regression
摘要：
本文介绍了一种混合频率下的高维线性IV回归方法。我们证明了利用低频工具变量可以识别和准确估计高频协变量的高维斜率参数。该模型的显着特点是，它允许处理高维数据集，而不施加近似稀疏性限制。我们提出了一个Tikhonov正则化估计，并推导出其时间序列数据的平均积分平方误差的收敛速度。该估计器具有一个封闭形式的表达式，易于计算，并在我们的蒙特卡罗实验中表现出优异的性能。我们估计了澳大利亚电力现货市场的实时价格弹性。我们的估计表明，供应是相对缺乏弹性，其弹性是异构的一整天。
英文摘要：
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.
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