Let $n$ be the estimator defined by: $$ n = \frac{1}{\text{sample mean}} $$
What is the mean squared error of the n estimator?
I assume there is no bias since the sample mean is an unbiased estimator.
Is this correct? $$ MSE(n) = \operatorname{Var}(n) = \sum_{i=1}^{n} \Bigl(\frac{1}{x_{i}} - \frac{1}{x_\text{sample mean}}\Bigr)^{2} $$
No.
if $\overline{X}_n$ is unbiased, say $\mathbb{E}[\overline{X}_n]=g(\theta)$ it is wellknown that $\frac{1}{\overline{X}_n}$ is biased (this is Jensen's inequality).
To calculate mean, variance, and MSE of $\frac{1}{\overline{X}_n}=\frac{n}{\sum_i X_i}$ you have to know the distribution on $\sum_i X_i$
Sometimes mean and variance $\frac{1}{\overline{X}_n}=\frac{n}{\sum_i X_i}$ can be derived with some brainstomings on the score of the model (for example using Bartlett's identities)