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Causal Panel Models with Sequential Exogeneity: a Balancing Approach

19th National Competition for Economic Research Grants

Economic analysis

Senior Researcher : Dmitry Arkhangelskiy

Research Centre or Institution : Fundación Centro de Estudios Monetarios y Financieros (CEMFI)


In this project, we focus on the estimation of causal effects with panel data under the assumption of sequential exogeneity. Sequential exogeneity is mostly ignored in applied papers that focus on estimating treatment effects, despite being a well-known and widely recognized econometric concept. Instead, most empirical work is done under a more restrictive (and often implicit) assumption of strict exogeneity. For example, this is true whenever policy effects are estimated using ordinary least squares (OLS) estimator with fixed effects.

This project's primary goal is to provide a flexible alternative to OLS and show that sequential exogeneity is a natural and useful concept for empirical work. To achieve this goal, we follow three steps. First, using recent ideas from causal literature, we construct a flexible estimator that can be used instead of the standard OLS and provide its interpretation in a rich causal model. Second, we analyze this estimator's statistical properties in different regimes (long and short panels) and illustrate the main trade-offs one has to face. Finally, we apply the new estimator to recently published empirical papers that use a standard OLS approach to see if sequential exogeneity changes the results in a significant way.

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