Dear Steve, Thank you for your answer! Basically the model performs well. However, I was comparing different specifications and also tried to replicate the results in Stata. I’m getting inconsistent results and I’m wondering which results are correct. I’m especially interested if I can trust the standard errors on the state level. After I’ve read the paper from Bertrand et al. on diff-in-diff estimates, I was wondering if I can interpret a significant result of the state-policy variable directly as “effective policy measure”. Does the given SAS specification fully control for intraclass correlation on state level (called clustering in Stata)? I also tried to replicate the code in Stata and Stata reports far higher standard errors: xtreg revenue facility-specific-variables i.year state-policy-measure, fe i(facility) cluster(state) and xtset facility year xtregar revenue facility-specific-variables state-policy-measure year-dummy, fe (followed by a jackknife procedure to adjust standard errors for clustering on state level) Sure, those specifications are slightly different than the SAS-specification (I wasn’t able to replicate the procedure 1:1 in Stata). However, the standard errors are about the factor 100 higher and therefore most of my effects are changing to insignificance at the 5%-level. I’ve 18 periods, 47 states and about 12,000 facilities in my sample. The Bertrand paper argues (more or less) that we have to exploit the variation on the state level and not the variation across all the facilities as they are correlated. In other words, if we assume a correlation of 1 between the facilities within a state, we have just 18 x 47 observations in the dataset. This makes the finding of significant results far less likely. Therefore, I’m wondering why SAS reports p-values for the state-policy variable of <.0001 while Stata reports around .05 (or higher). Regarding the suggestion of an interaction effect: I assume that most of the firm-specifics remain constant (controlled for with a fixed effect in the repeated statement), while some firm-specifics vary over time (it’s an 18 year panel). The state-policy-measure is a binary variable and varies over time. The firms are within states and do not change location. I’m not really sure why you would need an interaction effect. Where would you include one? StatePolicy*facility? facility*time? How would you interpret this effect? The dataset is unbalanced and has time gaps on the facility level. Bertrand, Marianne, Esther Duflo, and Sendhil Mullainathan. How much should we trust differences-in-differences estimates?. No. w8841. National Bureau of Economic Research, 2002. http://www.nber.org/papers/w8841.pdf,
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