<p>Functions of your original independent variables just yield new independent variables. Yes, you might know particular interpretations of the transformations, but the math neither knows nor cares.</p>
<p>If you have an approach to calculating the standard error of a regression coefficient, then use it.</p>
<p><a href="https://stats.stackexchange.com/a/545160/247274">I have written about this on the statistics Stack</a> and like the video I linked in my answer.</p>

CC BY-SA 4.0
2022-09-13T15:31:22.513Z
2022-09-13T15:31:22.513Z
Dave
<p>In a typical regression discontinuity design, the standard error of the discontinuity is <span class="math-container">$se(\beta_2)$</span> in the following model:</p>
<p><span class="math-container">$$Y = \beta_0 + \beta_1 X_1 + \beta_2 D + \beta_3 (D X_1) + U$$</span></p>
<p>However, how is the same standard error calculated when using local regression (i.e. weighted OLS with a linear or gaussian kernel)?</p>

<econometrics><regression>
How is the standard error of the discontinuity calculated when using local regression in a discontinuous regression?
Arturo Sbr
# How is the standard error of the discontinuity calculated when using local regression in a discontinuous regression?

(Asked by: username_0 on 2022-09-05)

In a typical regression discontinuity design, the standard error of the discontinuity is $se(\beta\_2)$ in the following model:

$$Y = \beta\_0 + \beta\_1 X\_1 + \beta\_2 D + \beta\_3 (D X\_1) + U$$

However, how is the same standard error calculated when using local regression (i.e. weighted OLS with a linear or gaussian kernel)?

**Tags:** econometrics, regression


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## 1 answers

### Answer by username_1 on 2022-09-13. Score: 0

Functions of your original independent variables just yield new independent variables. Yes, you might know particular interpretations of the transformations, but the math neither knows nor cares.

If you have an approach to calculating the standard error of a regression coefficient, then use it.

[I have written about this on the statistics Stack](https://stats.stackexchange.com/a/545160/247274) and like the video I linked in my answer.


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