  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.
