rregressionrddcausalityimpact-analysis

Obtaining covariates' estimates in rdrobust package


I am using rdrobust to estimate RDDs and for a submission in a journal the journal demands I report tables with covariates and their estimates. I don't think these should be reported in designs like these and don't really know how informative they are, but anyways: I can't find them anywhere in the output of the rdrobust call, so I was wondering whether there is anyway of actually obtaining them.

Here's my code:

library(rdrobust)
rd <- rdrobust(y = full_data$share_female,
         x = full_data$running,
         c = 0,
         cluster = full_data$constituency.name,
         covs=cbind(full_data$income, full_data$year_fct,
                    full_data$population, as.factor(full_data$constituency.name)))

I then call the object

rd

And get:

Call: rdrobust

Number of Obs.                 1812
BW type                       mserd
Kernel                   Triangular
VCE method                       NN

Number of Obs.                 1452          360
Eff. Number of Obs.             566          170
Order est. (p)                    1            1
Order bias  (q)                   2            2
BW est. (h)                   0.145        0.145
BW bias (b)                   0.221        0.221
rho (h/b)                     0.655        0.655
Unique Obs.                    1452          360

So as you see there seems to be no information on this on the output nor the object the function calls. I don't really know what to do. Thanks!


Solution

  • Unfortunately, I do not believe rdrobust() allows you to recover the coefficients introduced through the covs option.

    In your case, running the code as you provided and then running:

    rd$coef
    

    will only give you the point estimate for the rd estimator.

    Josh McCrain has written-up a nice vignette here to replicate rdrobust using lfe that also allows you to recover the coefficients on covariates.

    It involves some modification on your part and is of course not as user friendly, but does allow recovery of covariates.

    This might be beside the point by now, but the journal requirement in an RD design is odd.