Error computing Robust Standard errors in Panel regression model (plm,R) -


i using plm library run fixed effect regressions , sandwich,lmtest libraries compute robust standard errors. have no problem running regressions, in instances when go compute standard errors following error:

library(plm) library(sandwich) library(lmtest)  fe_reg <- plm(y ~ x + i(x^2)+factor(date), data=reg_data, index=c("id","date"), model="within") coeftest(fe_reg, vcov.=vcovhc(fe_reg, type="hc1"))  rruntimeerror: error in solve.default(crossprod(demx))   system computationally singular: reciprocal condition number = 1.84726e-25 

i not have problems computing coefficients or "normal" standard errors (ie homoscedastic). also, have no problem computing robust standard errors when omit quadratic term:

fe_reg <- plm(y ~ x +factor(date), data=reg_data, index=c("id","date"), model="within")   

anyone have idea going on? if design matrix singular coefficients should not have been computed, don't understand issue coming when computing standard errors.

thanks!

if remember correctly, plm not verbose. think possible have singular matrix plm not complain. lm more verbose. try

 lm_mod1 <- lm(y ~ x + i(x^2)+factor(date), data=reg_data)  summary(lm_mod1) 

lmwill tell in it's summary output if there problem calculating 1 coefficient (coefficient na in table , there should note @ top of output well). edit: note @ top of lm's summary output should "coefficients: (1 not defined because of singularities)" in case.

edit there possibility why coeftest not working: if model.matrix contrains large values small values, solve might not able solve system of linear equations computation in vcovhc function. thus, have @ model.matrix(y ~ x + i(x^2)+factor(date), data=reg_data) if case. if so, try rescaling variables (e.g. multiply oder divide 100 oder 1000 [also log() makes sense sometimes). take care, interpretation of coefficients changes due change of scales!


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