Post-Instrument Bias in Linear Models
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| Title: | Post-Instrument Bias in Linear Models |
|---|---|
| Language: | English |
| Authors: | Adam N. Glynn, Miguel R. Rueda (ORCID |
| Source: | Sociological Methods & Research. 2024 53(4):1829-1845. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 17 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Information Analyses |
| Descriptors: | Causal Models, Statistical Inference, Error of Measurement, Least Squares Statistics, Statistical Bias, Mathematical Models, Comparative Analysis, Mathematical Formulas |
| DOI: | 10.1177/00491241231156965 |
| ISSN: | 0049-1241 1552-8294 |
| Abstract: | Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, and ordinary least squares. In large samples and when the model provides a reasonable approximation, these formulas sometimes allow the analyst to bracket the parameter of interest with two estimators and allow the analyst to choose the estimator with the least asymptotic bias. We illustrate these points with a discussion of the settler mortality IV used by Acemoglu, Johnson, and Robinson. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1444065 |
| Database: | ERIC |
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| Abstract: | Post-instrument covariates are often included as controls in instrumental variable (IV) analyses to address a violation of the exclusion restriction. However, we show that such analyses are subject to biases unless strong assumptions hold. Using linear constant-effects models, we present asymptotic bias formulas for three estimators (with and without measurement error): IV with post-instrument covariates, IV without post-instrument covariates, and ordinary least squares. In large samples and when the model provides a reasonable approximation, these formulas sometimes allow the analyst to bracket the parameter of interest with two estimators and allow the analyst to choose the estimator with the least asymptotic bias. We illustrate these points with a discussion of the settler mortality IV used by Acemoglu, Johnson, and Robinson. |
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| ISSN: | 0049-1241 1552-8294 |
| DOI: | 10.1177/00491241231156965 |
