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Inference in the Presence of Weak Instruments: A Selected Survey

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Abstract

Here we present a selected survey in which we attempt to break down the ever burgeoning literature on inference in the presence of weak instruments into issues of estimation, hypothesis testing and confidence interval construction. Within this literature a variety of different approaches have been adopted and one of the contributions of this survey is to examine some of the links between them. The vehicle that we will use to establish these links will be the small concentration results of Poskitt and Skeels (2007), which can be used to characterize various special cases when instruments are weak. We make no attempt to provide an exhaustive survey of all of the literature related to weak instruments. Contributions along these lines can be found in, inter alia , Stock et al. (2002), Dufour (2003), Hahn and Hausman (2003), and Andrews and Stock (2007), and we view this survey as complementary to those earlier works.

Suggested Citation

  • Poskitt, D. S. & Skeels, C. L., 2013. "Inference in the Presence of Weak Instruments: A Selected Survey," Foundations and Trends(R) in Econometrics, now publishers, vol. 6(1), pages 1-99, August.
  • Handle: RePEc:now:fnteco:0800000017
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    File URL: http://dx.doi.org/10.1561/0800000017
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    Cited by:

    1. Firmin Doko Tchatoka & Jean-Marie Dufour, 2016. "Exogeneity tests, weak identification, incomplete models and non-Gaussian distributions: Invariance and finite-sample distributional theory," School of Economics Working Papers 2016-01, University of Adelaide, School of Economics.
    2. Firmin Doko Tchatoka & Wenjie Wang, 2015. "On Bootstrap Validity for Subset Anderson-Rubin Test in IV Regressions," School of Economics Working Papers 2015-01, University of Adelaide, School of Economics.

    More about this item

    Keywords

    Weak instruments; Linear simultaneous equation models; Instrument variables estimation; Large-sample asymptotic analysis; Finite-sample analysis; Hypothesis testing;

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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