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Bootstrap Inference on a Factor Model Based Average Treatment Effects Estimator

Author

Listed:
  • Luya Wang
  • Jeffrey S. Racine
  • Qiaoyu Wang

Abstract

We propose a novel bootstrap procedure for conducting inference for factor model based average treatment effects estimators. Our method overcomes bias inherent to existing bootstrap procedures and substantially improves upon existing large sample normal inference theory in small sample settings. The finite sample improvements arising from the use of our proposed procedure are illustrated via a set of Monte Carlo simulations, and formal justification for the procedure is outlined.

Suggested Citation

  • Luya Wang & Jeffrey S. Racine & Qiaoyu Wang, 2024. "Bootstrap Inference on a Factor Model Based Average Treatment Effects Estimator," Department of Economics Working Papers 2024-03, McMaster University.
    • Handle: RePEc:mcm:deptwp:2024-03
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    File URL: http://socialsciences.mcmaster.ca/econ/rsrch/papers/archive/2024-03.pdf
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    More about this item

    Keywords

    finite sample bias; average treatment effects; bootstrap inference; factor model;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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