High-Dimensional Mixed-Frequency IV Regression

42 Pages Posted: 22 May 2020 Last revised: 25 May 2021

See all articles by Andrii Babii

Andrii Babii

University of North Carolina at Chapel Hill

Date Written: March 2, 2020


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 study its large sample properties 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 also provide the confidence bands and incorporate the exogenous covariates via the double/debiased machine learning approach. In our empirical illustration, we estimate the real-time price elasticity of supply on the Australian electricity spot market. Our estimates suggest that the supply is relatively inelastic throughout the day.

Keywords: Tikhonov regularization, real-time price elasticity, electricity supply, confidence bands, double/debiased machine learning

JEL Classification: C14, C22, C26, C58

Suggested Citation

Babii, Andrii, High-Dimensional Mixed-Frequency IV Regression (March 2, 2020). Journal of Business and Economic Statistics, Forthcoming, Available at SSRN: https://ssrn.com/abstract=3579060 or http://dx.doi.org/10.2139/ssrn.3579060

Andrii Babii (Contact Author)

University of North Carolina at Chapel Hill ( email )

Gardner Hall, CB 3305
Chapel Hill, NC 27514
United States

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