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From |
andreas nordset <[email protected]> |

To |
[email protected] |

Subject |
st: Local Linear Regression for Regression Discontinuity Designs |

Date |
Sun, 22 May 2011 15:20:34 +0200 |

Dear Statalist members, in a context in which individuals are eligible for a treatment if and only if they are aged above 50, I would like to implement a Regression Discontinuity Design to estimate the effect of the treatment on several outcomes, i.e. the difference between the average outcome just above the threshold and the average outcome just below the threshold, where these averages must be estimated. My impression is that the standard way of doing this is to use "Local Linear Regression". My understanding is that I can hence obtain the Reduced-Form effect by simply estimating: -reg outcome D50 age D50_age if inrange(age,50-h,50+h)- where D50 is a dummy for being aged above 50, D50_Age is the interaction of that dummy with age, and h is the bandwidth. Equivalently, I would obtain the Wald estimates with: -ivreg2 outcome age D50_age (treatment=D50) if inrange(age,50-h,50+h)-. Put differently, my understanding of "Local Linear Regression" is to estimate simple linear OLS regressions, but a separate line on each side and only "locally", i.e. using only observations from the interval (50-h,50+h). Yet when I do so, I obtain estimates that differ from those obtained using Austin Nichol's -rd- command that apparently uses the -lpoly- command for local linear regression. Does that mean that my understanding of LLR is incorrect, maybe because some more sophisticated weighting of observations is needed? In your view, is such a more sophisticated procedure needed, and if so what would be the problems with my very simple procedure? Thank you so much for your advice and best regards! * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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