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Parsimonious estimation with many instruments

Author

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  • Jan J. J. Groen
  • George Kapetanios

Abstract

We suggest a way to perform parsimonious instrumental variables estimation in the presence of many, and potentially weak, instruments. In contrast to standard methods, our approach yields consistent estimates when the set of instrumental variables complies with a factor structure. In this sense, our method is equivalent to instrumental variables estimation that is based on principal components. However, even if the factor structure is weak or nonexistent, our method, unlike the principal components approach, still yields consistent estimates. Indeed, simulations indicate that our approach always dominates standard instrumental variables estimation, regardless of whether the factor relationship underlying the set of instruments is strong, weak, or absent.

Suggested Citation

  • Jan J. J. Groen & George Kapetanios, 2009. "Parsimonious estimation with many instruments," Staff Reports 386, Federal Reserve Bank of New York.
  • Handle: RePEc:fip:fednsr:386
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    Cited by:

    1. M. E. Bontempi & I. Mammi, 2012. "A strategy to reduce the count of moment conditions in panel data GMM," Working Papers wp843, Dipartimento Scienze Economiche, Universita' di Bologna.
    2. M. E. Bontempi & I. Mammi, 2014. "pca2: implementing a strategy to reduce the instrument count in panel GMM," Working Papers wp960, Dipartimento Scienze Economiche, Universita' di Bologna.
    3. Travaglini, Guido, 2010. "Supervised Principal Components and Factor Instrumental Variables. An Application to Violent CrimeTrends in the US, 1982-2005," MPRA Paper 22077, University Library of Munich, Germany.

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    Keywords

    Regression analysis ; Statistical methods ; Econometrics;

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