arXiv 20 Oct 2020 · Econometrics · 1 citations (OpenAlex)
arXiv:2010.10484 · PDF · DOI · OpenAlex · Extracted main text
This paper revisits the simple, but empirically salient, problem of inference on a real-valued parameter that is partially identified through upper and lower bounds with asymptotically normal estimators. A simple confidence interval is proposed and is shown to have the following properties: - It is never empty or awkwardly short, including when the sample analog of the identified set is empty. - It is valid for a well-defined pseudotrue parameter whether or not the model is well-specified. - It involves no tuning parameters and minimal computation. Computing the interval requires concentrating out one scalar nuisance parameter. In most cases, the practical result will be simple: To achieve 95% coverage, report the union of a simple 90% (!) confidence interval for the identified set and a standard 95% confidence interval for the pseudotrue parameter. For uncorrelated estimators -- notably if bounds are estimated from distinct subsamples -- and conventional coverage levels, validity of this simple procedure can be shown analytically. The case obtains in the motivating empirical application (de Quidt, Haushofer, and Roth, 2018), in which improvement over existing inference methods is demonstrated. More generally, simulations suggest that the novel confidence interval has excellent length and size control. This is partly because, in anticipation of never being empty, the interval can be made shorter than conventional ones in relevant regions of sample space.
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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 | Andrews and Kwon (2019) Inference in Moment Inequality Models That Is Robust to Spurious Precision under Model Misspecification | 1.000 | 11 | 4 | 100% |
| 2 | Imbens and Manski (2004) Confidence Intervals for Partially Identified Parameters | 1.000 | 6 | 3 | 100% |
| 3 | de Quidt, Haushofer, and Roth (2018) Measuring and Bounding Experimenter Demand | 1.000 | 5 | 3 | 100% |
| 4 | Stoye (2009) More on Confidence Regions for Partially Identified Parameters self | 1.000 | 5 | 3 | 100% |
| 5 | Andrews and Soares (2010) Inference for Parameters Defined by Moment Inequalities Using Generalized Moment Selection | 0.811 | 4 | 2 | 100% |
| 6 | Molinari (2020) Microeconometrics with Partial Identification | 0.737 | 3 | 2 | 100% |
| 7 | Romano, Shaikh, and Wolf (2014) A Practical Two-Step Method for Testing Moment Inequalities | 0.737 | 3 | 2 | 100% |
| 8 | Andrews, Roth, and Pakes (2019) Inference for Linear Conditional Moment Inequalities | 0.511 | 2 | 1 | 100% |
| 9 | Cox and Shi (2020) Simple Adaptive Size-Exact Testing for Full-Vector and Subvector Inference in Moment Inequality Models | 0.511 | 2 | 1 | 100% |
| 10 | Andrews and Barwick (2012) Inference for Parameters Defined by Moment Inequalities: A Recommended Moment Selection Procedure | 0.405 | 1 | 1 | 100% |
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arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.