Inference in differences-in-differences with few treated groups and heteroskedasticity
We derive an inference method that works in differences-in differences settings with few treated and many control groups in the pres ence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group s...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2019 |
| País: | Brasil |
| Institución: | Instituição de Ensino Superior e de Pesquisa (INSPER) |
| Repositorio: | Repositório Institucional da INSPER |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.insper.edu.br:11224/5063 |
| Acceso en línea: | https://repositorio.insper.edu.br/handle/11224/5063 |
| Access Level: | acceso abierto |
| Palabra clave: | differences-in-differences inference heteroskedasticity clustering few clusters bootstrap synthetic control linear factor model |
| Sumario: | We derive an inference method that works in differences-in differences settings with few treated and many control groups in the pres ence of heteroskedasticity. As a leading example, we provide theoretical justification and empirical evidence that heteroskedasticity generated by variation in group sizes can invalidate existing inference methods, even in data sets with a large number of observations per group. In contrast, our inference method remains valid in this case. Our test can also be combined with feasible generalized least squares, providing a safeguard against mis specification of the serial correlation. |
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