In order to justify the interchange of the derivative and integral when differentiating a characteristic function, one can use the dominated convergence theorem:
$$\frac{d}{dt} \int e^{itx} P(dx) = \lim_{h \to 0} \frac{1}{h} \int (e^{ihx}-1) e^{itx} P(dx).$$
Since $|e^{ihx}-1| \le |hx|$, we have
$$\frac{1}{h} \int |e^{ihx}-1| P(dx) \le \int |x| P(dx),$$
so if we assume the random variable is in $L^1$, we may push the derivative under the integral. Similarly, if the random variable is in $L^k$, then we can push the $k$th derivative under the integral.
I am trying to find an analogous statement for moment generating functions, but I am having trouble generalizing the above argument. Under what conditions can we do this for MGFs? Any hints would be appreciated, but I would prefer an argument that uses dominated convergence rather than Leibniz's integral rule.
Best Answer
Denote by
$$M(t) := \int e^{tx} \, \mathbb{P}(dx)$$
the moment generating function of the measure $\mathbb{P}$.
Proof: