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High-dimensional Mixed-frequency IV Regression.pdf

hd_mfiv.pdf
Preview of High-dimensional mixed-frequency IV regression
🔗 Source: ababii.github.io
📊 Size: 1.32 MB
📄 Pages: 41 pages
⬇️ Downloads: 226

Summary

The model handles high-dimensional datasets without imposing approximate sparsity restrictions. A Tikhonov-regularized estimator is proposed and its large sample properties are studied for time series data. The estimator has a closed-form expression and demonstrates excellent performance in Monte Carlo experiments. The paper also provides confidence bands and incorporates exogenous covariates via the double/debiased machine learning approach. An empirical illustration estimates the real-time price elasticity of supply on the Australian electricity spot market, suggesting that the supply is relatively inelastic throughout the day.

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Description

The model handles high-dimensional datasets without imposing approximate sparsity restrictions.

Technical Information

  • File Format: PDF
  • File Size: 1.32 MB
  • Pages: 41
  • Language: EN
  • Total Downloads: 226
  • Last Updated: 6 days ago

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This PDF document about High-dimensional mixed-frequency IV regression provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into High-dimensional mixed-frequency IV regression.

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High-dimensional guideHigh-dimensional tutorialmixed-frequency guidemixed-frequency tutorialregression guideregression tutorial

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