Notice: On March 31, it was **announced** that Statalist is moving from an email list to a **forum**. The old list will shut down on April 23, and its replacement, **statalist.org** is already up and running.

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

From |
"Ariel Linden, DrPH" <ariel.linden@gmail.com> |

To |
<statalist@hsphsun2.harvard.edu> |

Subject |
re: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd" |

Date |
Tue, 11 Oct 2011 11:09:18 -0400 |

Hi Stefano, I am a bit confused by your variables. If I understand correctly, your X variable is previous job tenure which is ranges from 0-52 months and your cutoff is 36. However, your "treatment" is whether a person gets severance, which, I am assuming can be at any point along the X variable continuum? In the RD design, the cutoff is the treatment assignment, so to make it work, you'd have to have everyone at or above 36 months receive severance and everyone below 36 months not receive severance. I am not sure that is what you have done here? I am not an economist (I don?t even play one on television), but I am not sold on the premise that length of previous tenure is associated the outcome variable (unless it is mediated vis-à-vis the severance). I also assume that the size of the severance will be associated with the Y variable, and may or may not have a strong independent association with the X variable (the recent CEO of HP just got fired after a year on the job and got a multi-million dollar severance). Thus, the type of position (or perhaps salary level of previous job) will moderate the relationship. Therefore, I am not sure you have the right variables, or the right modeling approach here. Perhaps you should consider switching to a mediation (controlling for moderators) approach, or perhaps a time series approach with two or three variables, (a) length of previous job tenure, (b) length of time unemployed thereafter, (c) relative size of severance? I hope this helps Ariel Date: Mon, 10 Oct 2011 21:15:37 +0200 From: Stefano Lombardi <lombardi_stefano@fastwebnet.it> Subject: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd" Hello everybody, I have a big problem in computing a sharp regression discontinuity design via the "rd" function. I have read a number of papers about the underlying theory, but I cannot carry out even a very basic RD design.. Unfortunately I found very little information on Statalist and on the whole Internet as well.. Could you please give a hand? Every comment would be tremendously helpful. Here is my (labor economics) setting: "tenure_cat": discrete forcing variable, Z = last job tenure (in months = 13, 14, ..., 52) "severance": treatment, X_T = lump-sum severance payment "nonendur": outcome, y = non-employment duration (days between the layoff and the start of the new job) The cut-off is at Z_0 = 36 months (after three years of job tenure, a person who is laid off is going to receive a severance payment with probability 1). Does the severance payment cause a variation in the job search? I also have "mean_nonedur" = "nonedur" mean conditioned on "tenure_cat" (basically the mean of y for each month between 13 to 52) My aim is to set a RD design with the mean nonemployment duration in days against Z in months. My first best would be to estimate the outcome gap through a second or higher order polynomial. All the data "far" from the cut-off have already been manually eliminated, hence I simply need to run the RD design with all the available data. As very first step, I simply tried to run the following command: . rd nonedur sevpay ten_cat, z0(36) Three variables specified; jump in treatment at Z=36 will be estimated. Local Wald Estimate is the ratio of jump in outcome to jump in treatment. Assignment variable Z is ten_cat Treatment variable X_T is sevpay Outcome variable y is nonedur Estimating for bandwidth 9.826534218815946 A predicted value of treatment at cutoff lies outside feasible range; switching to local mean smoothing for treatment discontinuity. score variables for model __00000P contain missing values r(322); Probably is nonsense, but I also tried to run the same command with "mean_nonedur" instead of "nonedur".. same result from Stata. Could you give me any suggestion about this issue? Is there something related to the bandwidth choice? Thank you very much, Stefano Lombardi * * 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/

**Follow-Ups**:**Re: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd"***From:*Stefano Lombardi <lombardi_stefano@fastwebnet.it>

- Prev by Date:
**Re: st: Re: problem with nlsur quaids** - Next by Date:
**st: # of Obs. in -stcox- result** - Previous by thread:
**st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd"** - Next by thread:
**Re: st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd"** - Index(es):