26.4 Standard Errors

Serial correlation is a big problem in DiD because (Bertrand, Duflo, and Mullainathan 2004)

  1. DiD often uses long time series
  2. Outcomes are often highly positively serially correlated
  3. Minimal variation in the treatment variable over time within a group (e.g., state).

To overcome this problem:

  • Using parametric correction (standard AR correction) is not good.
  • Using nonparametric (e.g., block bootstrap- keep all obs from the same group such as state together) is good when number of groups is large.
  • Remove time series dimension (i.e., aggregate data into 2 periods: pre and post). This still works with small number of groups (See (Donald and Lang 2007) for more notes on small-sample aggregation).
  • Empirical and arbitrary variance-covariance matrix corrections work only in large samples.


Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. 2004. “How Much Should We Trust Differences-in-Differences Estimates?” The Quarterly Journal of Economics 119 (1): 249–75.
Donald, Stephen G, and Kevin Lang. 2007. “Inference with Difference-in-Differences and Other Panel Data.” The Review of Economics and Statistics 89 (2): 221–33.