How Much Should We Trust Differences-in-Differences Estimates?
When a paper used several data sets with different time spans we only recorded the shortest span. Page 7. SHOULD WE TRUST DIFFERENCES-INDIFFERENCES? 255 turn
How Much Should We Trust Differences-in-Differences Estimates?
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN-DIFFERENCES ESTIMATES? Marianne Bertrand. Esther Duflo. Sendhil Mullainathan. Working Paper 8841 http://www.nber.org/
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN
We then use Monte Carlo simulations to investigate how several alternative estimation tech- niques help solve this serial correlation problem. We show that
How Much Should We Trust Staggered Difference-In-Differences
First DiD estimates are unbiased in settings where there is a single treatment period
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN
We then use Monte Carlo simulations to investigate how several alternative estimation tech- niques help solve this serial correlation problem. We show that
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN
We then use Monte Carlo simulations to investigate how several alternative estimation tech- niques help solve this serial correlation problem. We show that
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN
Most papers that employ Differences-in-Differences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the.
How Much Should We Trust the Dictators GDP Estimates?
10 Thus even a small value of ? may actually hide substantial differences in economic structure and in the nature of economic growth across countries with
How Much Should We Trust Staggered Difference-In- Differences
We suggest finance and accounting researchers should interpret standard TWFE staggered DiD regression estimates with caution particularly in cases where
How Much Should We Trust Staggered Difference-In-Differences
In fact these estimates can produce the wrong sign altogether compared to the true average treatment effects. We then describe three alternative estimators for
NBER WORKING PAPER SERIES HOW MUCH SHOULD WE TRUST
For each law we use OLS to compute the DD estimate of its “effect” as well as the standard error for this estimate The standard errors are severely biased: with about 20 years of data DD estimation finds an “effect” significant at the 5 level of up to 45 of the placebo laws
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN-DIFFERENCES ESTIMATES?
HOW MUCH SHOULD WE TRUST DIFFERENCES-IN-DIFFERENCES ESTIMATES?? Marianne Bertrand Esther Du?o Sendhil Mullainathan This Version: June 2003 Abstract Most papers that employ Di?erences-in-Di?erences estimation (DD) use many years of data and focus on serially correlated outcomes but ignore that the resulting standard errors are incon- sistent
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