Joshua Angrist, Michal Kolesár
arXiv 20 Oct 2021 · Econometrics · publishedJournal of Econometrics (2023) · 55 citations (OpenAlex)
arXiv:2110.10556 · PDF · DOI · OpenAlex · Extracted main text
We revisit the finite-sample behavior of single-variable just-identified instrumental variables (just-ID IV) estimators, arguing that in most microeconometric applications, the usual inference strategies are likely reliable. Three widely-cited applications are used to explain why this is so. We then consider pretesting strategies of the form $t_{1}>c$, where $t_{1}$ is the first-stage $t$-statistic, and the first-stage sign is given. Although pervasive in empirical practice, pretesting on the first-stage $F$-statistic exacerbates bias and distorts inference. We show, however, that median bias is both minimized and roughly halved by setting $c=0$, that is by screening on the sign of the estimated first stage. This bias reduction is a free lunch: conventional confidence interval coverage is unchanged by screening on the estimated first-stage sign. To the extent that IV analysts sign-screen already, these results strengthen the case for a sanguine view of the finite-sample behavior of just-ID IV.
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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 | Staiger, Douglas, Stock, James H (1997) Instrumental Variables Regression with Weak Instruments | 1.000 | 7 | 3 | 100% |
| 2 | Lee, David S., McCrary, Justin, Moreira, Marcelo J., Porter, Jack (2022) Valid $t$-Ratio Inference for IV | 1.000 | 7 | 3 | 100% |
| 3 | Andrews, Isaiah, Armstrong, Timothy B (2017) Unbiased Instrumental Variables Estimation under Known First-Stage Sign | 0.961 | 9 | 4 | 89% |
| 4 | Stock, James H., Yogo, Motohiro, Andrews, Donald W. K., Stock, James H (2005) Testing for Weak Instruments in Linear IV Regression | 0.941 | 6 | 4 | 83% |
| 5 | Keane, Michael, Neal, Timothy (2022) A Practical Guide to Weak Instruments | 0.874 | 6 | 2 | 100% |
| 6 | Andrews, Isaiah, Stock, James H., Sun, Liyang (2019) Weak Instruments in Instrumental Variables Regression: Theory and Practice | 0.811 | 4 | 2 | 100% |
| 7 | Bound, John, Jaeger, David A., Baker, Regina M (1995) Problems with Instrumental Variables Estimation When the Correlation Between the Instruments and the Endogenous Explanatory Vari… | 0.811 | 4 | 2 | 100% |
| 8 | Bekker, Paul A (1994) Alternative Approximations to the Distributions of Instrumental Variable Estimators | 0.737 | 3 | 2 | 100% |
| 9 | Hall, Alastair R., Rudebusch, Glenn D., Wilcox, David W (1996) Judging Instrument Relevance in Instrumental Variables Estimation | 0.644 | 2 | 2 | 100% |
| 10 | Angrist, Joshua D (1990) Lifetime Earnings and the Vietnam Era Draft Lottery: Evidence from Social Security Administrative Records self | 0.585 | 3 | 1 | 100% |
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