arXiv 24 Mar 2021 · Econometrics · 1 citations (OpenAlex)
arXiv:2103.13369 · PDF · DOI · OpenAlex · Extracted main text
We consider the setting in which a strong binary instrument is available for a binary treatment. The traditional LATE approach assumes the monotonicity condition stating that there are no defiers (or compliers). Since this condition is not always obvious, we investigate the sensitivity and testability of this condition. In particular, we focus on the question: does a slight violation of monotonicity lead to a small problem or a big problem? We find a phase transition for the monotonicity condition. On one of the boundary of the phase transition, it is easy to learn the sign of LATE and on the other side of the boundary, it is impossible to learn the sign of LATE. Unfortunately, the impossible side of the phase transition includes data-generating processes under which the proportion of defiers tends to zero. This boundary of phase transition is explicitly characterized in the case of binary outcomes. Outside a special case, it is impossible to test whether the data-generating process is on the nice side of the boundary. However, in the special case that the non-compliance is almost one-sided, such a test is possible. We also provide simple alternatives to monotonicity.
appendix boundary found by appendix_command · 57% of the source is main text. Read the extracted text to check this.
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 | Angrist, J. and Evans, W (1998) Children and their parents' labor supply: Evidence from exogenous variation in family size | 0.874 | 7 | 2 | 100% |
| 2 | Angrist, J. D., Imbens, G. W., and Rubin, D. B (1996) Identification of causal effects using instrumental variables | 0.511 | 2 | 1 | 100% |
| 3 | Imbens, G. W (2014) Instrumental variables: an econometrician's perspective | 0.511 | 2 | 1 | 100% |
| 4 | Soczyński, T (2020) When should we (not) interpret linear iv estimands as late? | 0.405 | 1 | 1 | 100% |
| 5 | Leeb, H. and Pötscher, B. M (2005) Model selection and inference: Facts and fiction | 0.405 | 1 | 1 | 100% |
| 6 | Stock, J. H (1991) Confidence intervals for the largest autoregressive root in US macroeconomic time series | 0.405 | 1 | 1 | 100% |
| 7 | Abadie, A., Angrist, J., and Imbens, G (2002) Instrumental variables estimates of the effect of subsidized training on the quantiles of trainee earnings | 0.405 | 1 | 1 | 100% |
| 8 | Abadie, A (2003) Semiparametric instrumental variable estimation of treatment response models | 0.405 | 1 | 1 | 100% |
| 9 | Andrews, D. W (1999) Estimation when a parameter is on a boundary | 0.405 | 1 | 1 | 100% |
| 10 | Andrews, D. W. and Guggenberger, P (2009) Validity of subsampling and" plug-in asymptotic" inference for parameters defined by moment inequalities | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 28 scored citations.