Replication code: Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators
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| Natura: | Recurso digital |
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Zenodo
2026
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| _version_ | 1866901726773116928 |
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| author | Gerber, Isaac |
| author_facet | Gerber, Isaac |
| contents | <p>This repository contains the simulation and empirical-illustration code that produces the numerical results in Gerber (2026), <em>Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators</em> (<a href="https://arxiv.org/abs/2605.04124">arXiv:2605.04124</a>).</p><p>The pipeline runs a 66,000-replication Monte Carlo over four scenarios (unconditional parallel trends with complex survey design, informative sampling with heterogeneous treatment effects, repeated cross-sections, and conditional parallel trends) and an empirical illustration on NHANES data. Each replication evaluates three inference paths: HC1 standard errors, weighted point estimate with PSU-level clustering, and full design-based standard errors with strata, PSU, and finite-population corrections.</p><p>Companion package: <a href="https://doi.org/10.5281/zenodo.19803705">diff-diff v3.3.2</a>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20097361 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Replication code: Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators Gerber, Isaac difference-in-differences survey statistics design-based variance influence functions Binder linearization Taylor series linearization complex survey design Monte Carlo replication Python <p>This repository contains the simulation and empirical-illustration code that produces the numerical results in Gerber (2026), <em>Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators</em> (<a href="https://arxiv.org/abs/2605.04124">arXiv:2605.04124</a>).</p><p>The pipeline runs a 66,000-replication Monte Carlo over four scenarios (unconditional parallel trends with complex survey design, informative sampling with heterogeneous treatment effects, repeated cross-sections, and conditional parallel trends) and an empirical illustration on NHANES data. Each replication evaluates three inference paths: HC1 standard errors, weighted point estimate with PSU-level clustering, and full design-based standard errors with strata, PSU, and finite-population corrections.</p><p>Companion package: <a href="https://doi.org/10.5281/zenodo.19803705">diff-diff v3.3.2</a>.</p> |
| title | Replication code: Design-Based Variance Estimation for Modern Heterogeneity-Robust Difference-in-Differences Estimators |
| topic | difference-in-differences survey statistics design-based variance influence functions Binder linearization Taylor series linearization complex survey design Monte Carlo replication Python |
| url | https://doi.org/10.5281/zenodo.20097361 |