Seminario CEDE - Pedro Pichetti

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The instrumental variables (IV) method has been widely studied in cross-sectional settings. However, many practical applications involve panel data, in settings where a unit’s treatment status may turn on or off over time. I show that in the presence of dynamic treatment effects, i.e., if past treatments affect current potential outcomes, standard methods are no longer valid if the instruments are serially correlated. This paper shows the nonparametric identification of dynamic causal effects in a potential outcomes framework in which potential outcomes depend on the treatment path taken by a unit through time but the first stage is static, in the sense that at each period the IV only instruments its contemporary treatment. I provide a nonparametric estimator that is consistent over the randomization distribution and derive its finite-population limiting distribution as the sample size increases. Monte Carlo Simulations illustrate the desirable finite-sample properties of the estimators. An application of the estimator show that law enforcement curbs illegal deforestation until two years after the actual detection of illegal practice, but effects fade out afterwards.