Hi,
I was pointed by a request on R-help to the following problem with
ar.ols():
R> set.seed(1)
R> x <- matrix(rnorm(4 * 2), ncol = 2)
R> ar.ols(x, order.max = 1, aic = FALSE, demean = FALSE)
Error in if ((dimension < 1) | (dimension > n)) stop("wrong embedding
dimension") :
argument is of length zero
In addition: Warning message:
In log(det(varE[[m - order.min + 1L]])) : NaNs produced
This happens on my 32-bit Debian (i686-pc-linux-gnu), both in R-release
and R-devel. The source is a numerical instability in the computations of
the error variance and subsequent AIC.
YH <- A[[m - order.min + 1L]] %*% t(X)
E <- (Y - YH)
varE[[m - order.min + 1L]] <- E %*% t(E)/N
[...]
aic[m - order.min + 1L] <- n.used * log(det(varE[[m -
order.min + 1L]])) + 2 * nser * (nser * m + intercept)
varE is the cross-product of the errors E and should be positive-definite
but here det(varE[[1]]) is -6.920697e-17 (on my machine) and thus taking
logs gives NaN yielding an aic of NaN for which the minimum cannot be
determined.
Of course, it does not make much sense to fit such a VAR model but either
a more meaningful error or a workaround would be useful. For example one
could take
log(max(0, det(varE[[m - order.min + 1L]])))
to avoid the negative determinant problem.
Best,
Z