Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.

[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

From |
D-Ta <altruist81@gmx.de> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: RD and binary outcomes |

Date |
Tue, 18 Oct 2011 18:45:20 +0200 |

Darjusch Am 18.10.2011 17:23, schrieb Austin Nichols:

D-Ta<altruist81@gmx.de>: It is true that -rd- uses only linear models, but this is by design. I have seen many papers using a parametric model, but it is not always clear that the parametric model is justified. The strength of the RD design is that it relies on so few assumptions; using a parametric model adds many more assumptions, and it is not always clear what they are. Think about what IV model you would run, using assignment to treatment (one side or the other of the cutoff) as an instrument for treatment received (for a linear model this is easy, otherwise not so much); how would you weight (i.e. what kernel and bandwidth choice would you make)? I would want some simulation evidence for any application of a parametric model to an RD design, using data built to look like the problem at hand, before I trusted the results. Smaller standard errors are often desirable not because they are closer to the truth, but because they lead to higher publication probabilities! In the case of RD, the large standard errors often reflect true uncertainty or variability of estimates, not simply the sample loss that accompanies focusing on the discontinuity. But I am sympathetic; looking only near the discontinuity increases internal validity at the cost of sample and leads many apparently strong effects to look statistically insignificant. Start with a good simulation, and make sure you put in some wiggles in the conditional mean functions (under treatment and no treatment). On Tue, Oct 18, 2011 at 9:41 AM, D-Ta<altruist81@gmx.de> wrote:Dear Stata-Users, I am currently working with the RD command provided by Austin Nichols. I investigate continous as well as binary outcome variables. I use cubic and quartic parametric models as benchmarks to compare the RD results against. In the case of continous variables the RD command does fine (i.e. very close to the parametric model with even smaller standard errors). However, when looking at binary outcomes, the RD procedure produces much wider standard errors and the point estimates differ a lot from the parametric (probit) specifications. I assume that this is due to the underlying local linear model used by RD. (Perhaps it would be better to use a local logit(?)) Am I right that -in its current format- the RD command is not the right tool to use in case of binary outcome variables? Many thanks Darjusch* * 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/

* * 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/

**References**:**st: RD and binary outcomes***From:*D-Ta <altruist81@gmx.de>

**Re: st: RD and binary outcomes***From:*Austin Nichols <austinnichols@gmail.com>

- Prev by Date:
**st: spmat object not found** - Next by Date:
**st: base category in margins output** - Previous by thread:
**Re: st: RD and binary outcomes** - Next by thread:
**st: i-1 in forvalues loop** - Index(es):