Hello, When the ar.ols function (in the package stats) is run with an argument that has a variance of zero, it returns an error: ar.ols(c(1,1,1)) Error in qr.default(x) : NA/NaN/Inf in foreign function call (arg 1) I believe that the reason is because the time series is automatically rescaled by the variance in these lines of the function: sc <- sqrt(drop(apply(x, 2L, var))) # Variance is zero x <- x/rep.int(sc, rep.int(n.used, nser)) # x is now c(NaN,NaN,NaN) There is no argument that forces the function not to rescale. A simple solution would be to verify that the variance is not zero before rescaling. I understand that fitting an autoregressive model to such a time series would not yield meaningful results. However, ar.ols is often used inside of other functions that may not be aware that the function will fail if the variance of time series is zero. If desired, I can post an example of how using a sandwich estimator can lead to calling ar.ols with a time series with a variance of zero. Is this a bug? If not, would it be appropriate to document this behaviour so that other functions could check the time series before passing them to ar.ols()? Perhaps another solution would be to allow users to force the function not to rescale the time series. My sessionInfo(): R version 2.15.0 (2012-03-30) Platform: i386-pc-mingw32/i386 (32-bit) locale: [1] LC_COLLATE=English_Canada.1252 LC_CTYPE=English_Canada.1252 LC_MONETARY=English_Canada.1252 LC_NUMERIC=C [5] LC_TIME=English_Canada.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base loaded via a namespace (and not attached): [1] tools_2.15.0 Thanks, Aman Verma