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A Shrinkage Instrumental Variable Estimator For Large Datasets

Author

Listed:
  • Andrea Carriero

    (Queen Mary - University of London)

  • George Kapetanios

    (Queen Mary - University of London)

  • Massilimiano Marcellino

    (Bocconi University)

Abstract

This paper proposes and discusses an instrumental variable estimator that can be of particular relevance when many instruments are available and/or the number of instruments is large relative to the total number of observations. Intuition and recent work (see, e.g., Hahn, 2002) suggest that parsimonious devices used in the construction of the final instruments may provide effective estimation strategies. Shrinkage is a well known approach that promotes parsimony. We consider a new shrinkage 2SLS estimator. We derive a consistency result for this estimator under general conditions, and via Monte Carlo simulation show that this estimator has good potential for inference in small samples.

Suggested Citation

  • Andrea Carriero & George Kapetanios & Massilimiano Marcellino, 2015. "A Shrinkage Instrumental Variable Estimator For Large Datasets," L'Actualité Economique, Société Canadienne de Science Economique, vol. 91(1-2), pages 67-87.
    • Handle: RePEc:ris:actuec:0113
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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