Is there a way to estimate Robust standard errors when using a nls()
function? I'm trying to fit some data to a complicated model and everything
works fine with nls() but I also wanted to obtain a robust estimate of my
errors.
I tried "coeftest(m, vcov=sandwich)" and it seems to work, but so does
"coeftest(m, vcov = NeweyWest(m, lag = 4))" or "coeftest(m, vcov =
kernHAC(m, kernel = "Bartlett", bw = 5, prewhite = FALSE, adjust =
FALSE))". They return different error estimates so I wanted you to help me
understand what I should do, if I'm doing something wrong and other stuff.
Thank you
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