Robust standard error estimation for the Hausman-Taylor estimator using plm() and vcovHC()

Question

Suppose I compute the Hausman-Taylor estimator using the plm command with the option: model= "ht" . Using the result I like to obtain a robust variance-covariance matrix to make inference fully robust. For this purpose the vcovHC() command (part of the plm package) is used. Here is a minimal example:

data("Wages", package = "plm")
ht <- plm(lwage ~ wks + south + smsa + married + exp + I(exp^2) +
        bluecol + ind + union + sex + black + ed | 
        sex + black + bluecol + south + smsa + ind,
      data = Wages, model = "ht", index = 595)

vcvHT <- vcovHC(ht,method="arellano")
Error in vcovHC.plm(ht, method = "arellano") : 
Model has to be either random, within or pooling model

Technically, as the error message indicates, vcovHC() is unable to compute the VCV matrix since it does not support models of the type computed by plm(...,model="ht")

My question is this:

Why doesn't vcovHC() support the Hausman-Taylor model? Is it because standard error based on a (cluster) robust VCV matrix shouldn't be used for theoretical reasons (inconsistent etc.), or is it simply not implemented but save to use (if programmed by hand)?

Answer 1

It is currently not implemented (yet); but as HT is a special kind of IV, it should in principle be possible to compute an HC covariance. I will get around to doing it sometime. A production version requires a lot of interface work and consideration of all possible cases; but an ad-hoc function might be relatively easy to write, based on components from the model object.