倍差法STATA命令.pdf

MPRA Simplifying the Estimation of Difference in Differences Treatment Effects with Stata* Juan M. Villa Brooks World Poverty Institute University of Manchester Manchester, UK. juan.villalora@postgrad.manchester.ac.uk *** DRAFT VERSION *** Abstract. This paper explains the insights of the Statas user written command diff for the estimation of Difference in Differences treatment effects (DID). The options and the formulas are detailed for the single DID, Kernel Propensity Score DID, Quantile DID and the balancing properties . An example of the features of diff is presented by using the dataset from Card and Krueger (1994). Keywords: Difference in differences, causal inference, kernel propensity score, quantile treatment effects, quasi-experiments. 1. Introduction Difference in Differences treatment effects (DID) have been widely used when the evaluation of a given intervention entails the collection of panel data or repeated cross sections. DID integrates the advances of the fixed effects estimators with the causal inference analysis when unobserved events or characteristics confound the interpretations (Angrist and Pischke, 2008). Despite the existence of other plausible methods based on the availability of observational data for quasi-experimental causal inference -i.e. matching methods, instrumental variable, regression discontinuity-, DID estimations offer an alternative reaching the unconfoundedness by controlling for unobserved characteristics and combining it with observed or complementary information. Additionally, the DID is a flexible form of causal inference because it c