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From:

A:

Download the patch here and install it manually:
This requires logon as an Administrator.

Q:

The definition of asymptotic variance

Wikipedia says asymptotic variance is the variance of the difference between the true value of the parameter and the estimator in the limit of large n. Is that also equivalent to the variance of the original parameter minus the variance of the estimator?

A:

These are equivalent.
If $X_1,…,X_n$ are $n$ independent random variables with $X_i \sim N(\theta,\sigma^2)$ and $\hat\theta_n$ the $M$-estimator, the asymptotic variance of $X_n$ will be $\lim_n Var\left(\frac{X_n-\theta}{\hat\sigma/\sigma}\right)$
Therefore, $$Var\left(\frac{X_n-\theta}{\hat\sigma/\sigma}\right)= \frac{1}{n}\sum_{i=1}^n\left(\frac{X_i-\theta}{\hat\sigma/\sigma}\right)^2$$
and the variance of $\frac{X_n-\theta}{\hat\sigma/\sigma}$ is the variance of $X_n$ minus the variance of $\frac{\hat\sigma}{\sigma}\hat\theta_n$. If you want to calculate $\lim_n\frac{Var(\hat\theta_n)}{Var(X_n)}$, you should consider the first expression of this difference and substract the second one.

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