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dqd: A command for treatment effect estimation under alternative assumptions

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  • Mora, Ricardo
  • Reggio, Iliana

Abstract

Conventional difference-in-differences (DID) methods that are used to estimate the effect of a treatment rely on important identifying assumptions. Identification of the treatment effect in a DID framework requires some assumption relating trends for controls and treated in absence of treatment, the most common being the assumption of Parallel Paths. When several pre-treatment periods are available, Mora and Reggio (2012) show that treatment effect identification does not uniquely depend on the Parallel Path assumption, but also on the trend modeling strategy. They further define a family of alternative Parallel assumptions and propose a more flexible model which can be a helpful starting tool to study robustness to alternative Parallel assumptions and trend dynamics. In this paper we introduce a Stata command that implements the fully flexible model presented in Mora and Reggio (2012), producing tests for the equivalence of alternative parallel assumptions and for the dynamic effects of the treatment. The standard DID in model with or without polynomial trends can also be obtained.

Suggested Citation

  • Mora, Ricardo & Reggio, Iliana, 2014. "dqd: A command for treatment effect estimation under alternative assumptions," UC3M Working papers. Economics we1407, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:we1407
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    1. Marianne Bertrand & Esther Duflo & Sendhil Mullainathan, 2004. "How Much Should We Trust Differences-In-Differences Estimates?," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(1), pages 249-275.
    2. Mora, Ricardo & Reggio, Iliana, 2012. "Treatment effect identification using alternative parallel assumptions," UC3M Working papers. Economics we1233, Universidad Carlos III de Madrid. Departamento de Economía.
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