Gabriel Okasa, Kenneth A. Younge
arXiv 14 Sep 2022 · Econometrics
arXiv:2209.06631 · PDF · DOI · OpenAlex · Extracted main text
Researchers frequently test and improve model fit by holding a sample constant and varying the model. We propose methods to test and improve sample fit by holding a model constant and varying the sample. Much as the bootstrap is a well-known method to re-sample data and estimate the uncertainty of the fit of parameters in a model, we develop Sample Fit Reliability (SFR) as a set of computational methods to re-sample data and estimate the reliability of the fit of observations in a sample. SFR uses Scoring to assess the reliability of each observation in a sample, Annealing to check the sensitivity of results to removing unreliable data, and Fitting to re-weight observations for more robust analysis. We provide simulation evidence to demonstrate the advantages of using SFR, and we replicate three empirical studies with treatment effects to illustrate how SFR reveals new insights about each study.
appendix boundary found by appendix_command · 86% 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 | Kuschnig, Nikolas, Zens, Gregor, Crespo Cuaresma, Jesús (2021) Hidden in Plain Sight: Influential Sets in Linear Models | 1.000 | 6 | 5 | 100% |
| 2 | Broderick, Tamara, Giordano, Ryan, Meager, Rachael (2020) An Automatic Finite-Sample Robustness Metric: When Can Dropping a Little Data Make a Big Difference? | 1.000 | 5 | 4 | 100% |
| 3 | Fischler, Martin A, Bolles, Robert C (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography | 0.843 | 3 | 3 | 100% |
| 4 | Angrist, Joshua D (2010) The credibility revolution in empirical economics: How better research design is taking the con out of econometrics | 0.737 | 3 | 2 | 100% |
| 5 | Moitra, Ankur, Rohatgi, Dhruv (2022) Provably Auditing Ordinary Least Squares in Low Dimensions | 0.737 | 3 | 2 | 100% |
| 6 | Athey, Susan, Imbens, Guido W (2019) Machine learning methods that economists should know about | 0.644 | 2 | 2 | 100% |
| 7 | Atkinson, Anthony C, Riani, Marco (2007) Building regression models with the forward search | 0.644 | 2 | 2 | 100% |
| 8 | Belsley, David A, Kuh, Edwin, Welsch, Roy E (1980) Regression diagnostics: Identifying influential data and sources of collinearity | 0.644 | 2 | 2 | 100% |
| 9 | DiCiccio, Thomas J, Efron, Bradley (1996) Bootstrap confidence intervals | 0.644 | 2 | 2 | 100% |
| 10 | Raguram, Rahul, Frahm, Jan-Michael, Pollefeys, Marc (2008) A comparative analysis of RANSAC techniques leading to adaptive real-time random sample consensus | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 58 scored citations.