I have a problem when I try to interpret my results from a regression discontinuity. I am using Stata 16 at a Mac. I am using the rdrobust plugin, with uniform kernel and a fixed bandwidth of 25000 and two controls. I have a hard time interpret the results as they show a conventional negative and significant, effect while at the same time show a positive, and significant, robust effect. Does anyone know why this might be the case?
I tried with several placebo-cut offs and they all seem to be significant in a different direction so I do not think that my results are any stable, but this switch between significant positive and negative between conventional and robust I've never seen before.
Code:
Code:
rdrobust percentage_sup distance, kernel(uniform) h(25000) covs(DIST_FDR turnout) p(1)
Covariate-adjusted sharp RD estimates using local polynomial regression.
Cutoff c = 0 | Left of c Right of c Number of obs = 5781202
-------------------+---------------------- BW type = Manual
Number of obs | 3081 10204 Kernel = Uniform
Eff. Number of obs | 1785 2265 VCE method = NN
Order est. (p) | 1 1
Order bias (q) | 2 2
BW est. (h) | 25000.000 25000.000
BW bias (b) | 25000.000 25000.000
rho (h/b) | 1.000 1.000
Outcome: percentage_sup. Running variable: distance.
--------------------------------------------------------------------------------
Method | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------------+------------------------------------------------------------
Conventional | -2.827 .369 -7.6615 0.000 -3.55027 -2.10383
Robust | - - 2.9830 0.003 .716598 3.46233
--------------------------------------------------------------------------------
Covariate-adjusted estimates. Additional covariates included: 2
0 Response to Positive and significant conventional CI but negative significant robust CI in RDD
Post a Comment