Method of moment estimator Pareto

I'm working on a Pareto distribution function, where I have to find the method of moments estimate of $\theta$. The function is:

$f(x|x_0, \theta) = \theta \cdot x^{\theta}_0 \cdot x^{-\theta-1}$

When $x > x_0$ and $\theta > 1$. Assume that $x_0 > 0$ is known. $\theta$ is unknown and a random sample $(X_1, X_2,...,X_n)$ satisfying a Pareto distribution is given.

At the moment I'm mostly a bit confused about the notation and variables, especially the $\theta$.

My approach so far is to use the following from my textbook:

That the $k$th moment of a random variable taken about the origin is

$\mu_k' = E(X^{k})$ and the corresponding $k$th sample moment is the average.

Relating the above to my pareto function, does $k$ corresponds to $\theta$?

Assume that $x_0>0$ is known.

The job is to express the desired parameter ($\theta$) as a function of $\mu$ than substitute $\mu$ with the sample mean $\overline{X}_n$

You know that

$$\mu=\frac{\theta x_0}{\theta-1}$$

that is

$$\theta=\frac{\mu}{\mu-x_0}$$

and thus, applying MoM:

$$\hat{\theta}_{\text{MM}}=\frac {\overline{X}_n}{\overline{X}_n-x_0}$$