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From |
Austin Nichols <austinnichols@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Regression discontinuity with interrupted time series |

Date |
Wed, 6 Mar 2013 16:51:24 -0500 |

Joshua Mitts <joshua.mitts@yale.edu>: You need to be a lot more clear about your scientific model of the data generating process. I have not read the cited paper, but I am doubtful about the marriage of RD and ITS. The point of RD is that outcomes of observations on either side of the cutoff are identical on average except for treatment status so the jump in outcomes at the cutoff is the effect of treatment; that is not true if you think treatment has some dynamic impacts, or in other words the effect of treatment increases (or decreases) in the assigment variable, so that you do not want the instantaneous impact of treatment at the cutoff, but some effect away from the cutoff. Imagine it this way: you have an announcement event that affects stock prices at noon but you do not want to compare stock prices at one minute after noon to noon because you think the event actually changes the time path of investment and you want changes in market valuation over some period until the announced policy change takes place. This is no longer a good situation for RD if you are thinking about comparing across time. You can still use the dummy for above the cutoff at time t as a dummy for treatment in periods after t, but the comparison will not have the clean RD interpretation (where the counterfactual is essentially observed if the data is dense around the cutoff) if you are using time as an assignment variable. Can you assume linear trends before and after time t? You can define time as time minus t so that the constant is the jump in mean outcomes at t, and the dummy for above the cutoff is the instrument for treatment; if they are perfectly collinear you have a "sharp" design but you may want to also estimate a change in trend after t for the treatment group, for which you may need an additional instrument--the key here is how the cutoff is defined. Is the variable that is compared to the cutoff subject to manipulation? Changing over time? Only examined at time t? If you have an assigment variable that is not time, you are back in the world of RD, and you may be better off with a long difference in outcomes (reducing problems due to measurement error in fixed effect models), e.g. y at t+5 minus y at t-5, regressed on treatment in the usual RD manner. On Wed, Mar 6, 2013 at 11:00 AM, Joshua Mitts <joshua.mitts@yale.edu> wrote: > Hi, > > How can I combine regression discontinuity with interrupted time > series analysis in Stata? I have repeated observations of an outcome > variable for ~180 units over time, an intervention at time t at a > cutoff value, and more repeated observations post-intervention. With > ordinary RD, I can only measure the outcome individually at t+1, t+2, > etc. It seems this is an active area of research[1], but I'm not sure > how to implement it in Stata. Any suggestions would be greatly > appreciated. > > Thank you, > Josh > > -- > Joshua Mitts > Yale Law School '13 > joshua.mitts@yale.edu > > [1] Somers, Maree-Andre, Pei Zhu, Robin Jacob, and Howard Bloom. 2009. > Combining Regression Discontinuity Analysis and Interrupted > Time-Series Analysis. Grant #R305D090009. Washington, DC: Institute of > Education Sciences, U. S. Department of Education. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/faqs/resources/statalist-faq/ * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Regression discontinuity with interrupted time series***From:*Joshua Mitts <joshua.mitts@yale.edu>

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