arXiv 4 Sep 2023 · Econometrics · publishedJournal of Econometrics (2025) · 5 citations (OpenAlex)
arXiv:2309.01637 · PDF · DOI · OpenAlex · Extracted main text
Montiel Olea and Pflueger (2013) proposed the effective F-statistic as a test for weak instruments in terms of the Nagar bias of the two-stage least squares (2SLS) estimator relative to a benchmark worst-case bias. We show that their methodology applies to a class of linear generalized method of moments (GMM) estimators with an associated class of generalized effective F-statistics. The standard nonhomoskedasticity robust F-statistic is a member of this class. The associated GMMf estimator, with the extension f for first-stage, is a novel and unusual estimator as the weight matrix is based on the first-stage residuals. As the robust F-statistic can also be used as a test for underidentification, expressions for the calculation of the weak-instruments critical values in terms of the Nagar bias of the GMMf estimator relative to the benchmark simplify and no simulation methods or Patnaik (1949) distributional approximations are needed. In the grouped-data IV designs of Andrews (2018), where the robust F-statistic is large but the effective F-statistic is small, the GMMf estimator is shown to behave much better in terms of bias than the 2SLS estimator, as expected by the weak-instruments test results.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Montiel Olea, J. L. and C. Pflueger (2013) A Robust Test for Weak Instruments | 1.000 | 19 | 6 | 100% |
| 2 | Andrews, I., J. H. Stock, and L. Sun (2019) Weak Instruments in Instrumental Variables Regression: Theory and Practice | 1.000 | 12 | 5 | 100% |
| 3 | Stock, J. H. and M. Yogo (2005) Testing for Weak Instruments in Linear IV Regression, in | 1.000 | 5 | 4 | 100% |
| 4 | Andrews, I (2018) Valid Two-Step Identification-Robust Confidence Sets for GMM | 0.984 | 21 | 8 | 95% |
| 5 | Nagar, A. L (1959) The Bias and Moment Matrix of the General k-Class Estimators of the Parameters in Simultaneous Equations | 0.737 | 3 | 3 | 67% |
| 6 | Lewis, D. J. and K. Mertens (2022) A Robust Test for Weak Instruments with Multiple Endogenous Regressors, Working Papers 2208, Federal Reserve Bank of Dallas | 0.644 | 2 | 2 | 100% |
| 7 | Staiger, D. and J. H. Stock (1997) Instrumental Variables Regression with Weak Instruments | 0.644 | 2 | 2 | 100% |
| 8 | StataCorp (2023) 2023 | 0.644 | 2 | 2 | 100% |
| 9 | Pflueger, C. E. and S. Wang (2015) A Robust Test for Weak Instruments in Stata | 0.644 | 2 | 2 | 100% |
| 10 | Patnaik, P. B (1949) The Non-Central Chi-Square and F-Distributions and Their Applications | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 24 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Two-Sample IV: Efficient Two-Step Estimation and Tests for Overidentification and Weak-Instruments | 0.843 | 3 | 3 |
| 2 | Double Machine Learning for Static Panel Data with Instrumental Variables: New Method and Applications | 0.644 | 2 | 2 |