arXiv 16 Sep 2021 · Econometrics · publishedThe Review of Economics and Statistics (2023) · 2 citations (OpenAlex)
arXiv:2109.08222 · PDF · DOI · OpenAlex · Extracted main text
We provide adaptive confidence intervals on a parameter of interest in the presence of nuisance parameters when some of the nuisance parameters have known signs. The confidence intervals are adaptive in the sense that they tend to be short at and near the points where the nuisance parameters are equal to zero. We focus our results primarily on the practical problem of inference on a coefficient of interest in the linear regression model when it is unclear whether or not it is necessary to include a subset of control variables whose partial effects on the dependent variable have known directions (signs). Our confidence intervals are trivial to compute and can provide significant length reductions relative to standard confidence intervals in cases for which the control variables do not have large effects. At the same time, they entail minimal length increases at any parameter values. We prove that our confidence intervals are asymptotically valid uniformly over the parameter space and illustrate their length properties in an empirical application to a factorial design field experiment and a Monte Carlo study calibrated to the empirical application.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | McCloskey, A (2017) Bonferroni-based size-correction for nonstandard testing problems self | 0.928 | 5 | 3 | 80% |
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| 3 | Kwon, K. and Kwon, S (2020) Inference in regression discontinuity designs under monotonicity | 0.874 | 5 | 2 | 100% |
| 4 | Ketz, P (2018) Subvector inference when the true parameter vector may be near or at the boundary self | 0.874 | 5 | 2 | 100% |
| 5 | Cai, T. T. and Low, M. G (2004) An adaptation theory for nonparametric confidence intervals | 0.811 | 4 | 2 | 100% |
| 6 | Armstrong, T. B. and Kolesár, M (2018) Optimal inference in a class of regression models | 0.737 | 4 | 3 | 50% |
| 7 | Muralidharan, K., Romero, M., and Wüthrich, K (2020) Factorial designs, model selection, and (incorrect) inference in randomized experiments | 0.737 | 3 | 2 | 100% |
| 8 | Berry, S., Levinsohn, J., and Pakes, A (1995) Automobile Prices in Market Equilibrium | 0.644 | 2 | 2 | 100% |
| 9 | Blattman, C., Jamison, J. C., and Sheridan, M (2017) Reducing crime and violence: Experimental evidence from cognitive behavioral therapy in Liberia | 0.644 | 2 | 2 | 100% |
| 10 | Armstrong, T. B., Kolesár, M., and Kwon, S (2020) Bias-aware inference in regularized regression models | 0.585 | 3 | 1 | 100% |
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