Francis J. DiTraglia, Camilo Garcia-Jimeno
arXiv 14 Nov 2020 · Econometrics · publishedJournal of Business and Economic Statistics (2020) · 13 citations (OpenAlex)
arXiv:2011.07276 · PDF · DOI · OpenAlex · Extracted main text
To estimate causal effects from observational data, an applied researcher must impose beliefs. The instrumental variables exclusion restriction, for example, represents the belief that the instrument has no direct effect on the outcome of interest. Yet beliefs about instrument validity do not exist in isolation. Applied researchers often discuss the likely direction of selection and the potential for measurement error in their articles but lack formal tools for incorporating this information into their analyses. Failing to use all relevant information not only leaves money on the table; it runs the risk of leading to a contradiction in which one holds mutually incompatible beliefs about the problem at hand. To address these issues, we first characterize the joint restrictions relating instrument invalidity, treatment endogeneity, and non-differential measurement error in a workhorse linear model, showing how beliefs over these three dimensions are mutually constrained by each other and the data. Using this information, we propose a Bayesian framework to help researchers elicit their beliefs, incorporate them into estimation, and ensure their mutual coherence. We conclude by illustrating our framework in a number of examples drawn from the empirical microeconomics literature.
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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 | DiTraglia, F., García-Jimeno, C (2019) Identifying the effect of a mis-classified, binary, endogenous regressor self | 0.843 | 3 | 3 | 100% |
| 2 | Gustafson, P (2015) Bayesian Inference for Partially Identified Models: Exploring the Limits of Limited Data | 0.737 | 3 | 2 | 100% |
| 3 | Acemoglu, D., Johnson, S., Robinson, J. A (2001) The colonial origins of comparative development: An empirical investigation | 0.693 | 7 | 1 | 100% |
| 4 | Becker, S. O., Woessmann, L (2009) Was Weber wrong? A human capital theory of Protestant economic history | 0.693 | 6 | 1 | 100% |
| 5 | Burde, D., Linden, L (2013) Bringing education to Afghan girls: A randomized controlled trial of village-based schools | 0.693 | 5 | 1 | 100% |
| 6 | Bollinger, C. R (2003) Measurement error in human capital and the black-white wage gap | 0.644 | 2 | 2 | 100% |
| 7 | Bollinger, C. R., van Hasselt, M (2017) Bayesian moment-based inference in a regression models with misclassification error | 0.644 | 2 | 2 | 100% |
| 8 | Frazis, H., Lowenstein, M. A (2003) Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables | 0.644 | 2 | 2 | 100% |
| 9 | Hu, Y (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 0.644 | 2 | 2 | 100% |
| 10 | Lewbel, A., March (2007) Estimation of average treatment effects with misclassification | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 40 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Salvaging Falsified Instrumental Variable Models | 0.000 | 1 | 1 |