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

 From Stefano Lombardi To statalist@hsphsun2.harvard.edu Subject st: Regression Discontinuity (RD) Designs, sharp discontinuity: basic question about implementation with "rd" Date Mon, 10 Oct 2011 21:15:37 +0200

```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
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