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High-dimensional mixed-frequency IV regression

Andrii Babii

arXiv 30 Mar 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2020) · 1 citations (OpenAlex)

arXiv:2003.13478 · PDF · DOI · OpenAlex · Extracted main text

Abstract

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.

Citation extraction

42
references
63
in-text mentions
42
distinct cited
4
self-citations
11,005
main-text words

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Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Marine Carrasco, Jean-Pierre Florens, and Eric Renault (2007) Linear inverse problems in structural econometrics estimation based on spectral decomposition and regularization1.00084100%
2David Benatia, Marine Carrasco, and Jean-Pierre Florens (2017) Functional linear regression with functional response0.84333100%
3Andrii Babii and Jean-Pierre Florens (2018) Is completeness necessary? Estimation and inference in non-identified models self0.64422100%
4Andrii Babii, Eric Ghysels, and Jonas Striaukas (2019) Estimation and HAC-based inference for machine learning time series regressions self0.64422100%
5Andrii Babii (2020) Honest confidence sets in nonparametric iv regression and other ill-posed models self0.64422100%
6Herman J Bierens (1982) Consistent model specification tests0.64422100%
7Marine Carrasco, Jean-Pierre Florens, and Eric Renault (2014) Asymptotic normal inference in linear inverse problems0.64422100%
8Jean-Pierre Florens and Sébastien Van Bellegem (2015) Instrumental variable estimation in functional linear models0.64422100%
9Maxwell B. Stinchcombe and Halbert White (1998) Consistent specification testing with nuisance parameters present only under the alternative0.64422100%
10Philip G. Wright (1928) Tariff on animal and vegetable oils0.64422100%

Showing the top 10 of 42 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Functional instrumental variable regression with an application to estimating the impact of immigration on native wages0.64422
2Functional Linear Projection and Impulse Response Analysis0.58531
3Functional Regression with Nonstationarity and Error Contamination: Application to the Economic Impact of Climate Change0.51121
4Average Marginal Effects in One-Step Partially Linear Instrumental Regressions0.51122
5Are Unobservables Separable?0.40511
6Machine Learning Panel Data Regressions with Heavy-tailed Dependent Data: Theory and Application0.40511