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One of the breakthroughs of econometrics over the past two decades has been to employ "clustering" to take into account the correlation of error terms across observations. For instance, if you're evaluating the effect of an educational intervention where you have data on individual students but you suspect that teachers implemented the intervention differently, it is common to analyze the data in a way that recognizes that there are common effects at the "class" level. A common correction is to use clustering.

When you run a regression discontinuity, do you similarly have to take into account that your observations may be clustered? If so, how is the estimator implemented differently?


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Could you give some references? It is a first time I hear about this breakthrough. Also what do you mean by regression discontinuity? –  mpiktas Oct 16 '11 at 10:19
Clustering first gained notoriety with the so-called "Moulton critique" discussed here: sciencedirect.com/science/article/pii/0304407686900217. A more recent example of the danger of not-clustering was presented in: nber.org/papers/w8841. You can read about RD here: en.wikipedia.org/wiki/Regression_discontinuity_design –  dchandler Oct 16 '11 at 14:48
@lejohn, yes I think too, but this is exactly the term OP used. If I understand correctly it is simply robust standard errors for panel data type models. –  mpiktas Nov 14 '11 at 9:14
-1 This question does not provide enough information to allow for a constructive answer. –  baha-kev Feb 27 '12 at 1:38

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