Are Cumulative Distribution Functions measurable? It is well-known that CDFs (Cumulative Distribution Functions) of one-dimensional random variables are Borel measurable. But does the same apply to CDFs of multi-dimensional random variables (rvecs)? It suffices, for my purposes, to consider finite dimensional rvecs.

Relevant definitions
Let $n$ be an integer $\geq 2$.
Call a probability measure over the Borel field on $\mathbb R^n$ an $n$-dimensional probability measure.
To every $n$-dimensional probability measure, $m$, define the following function $F:\mathbb R^n\rightarrow\mathbb R$, called $m$'s CDF: $F(x)=m\left((-\infty, x]\right)$ where $(-\infty, x]$ is the set of all $y$ in $\mathbb R^n$ such that $y\leq x$ component-wise.
An $n$-dimensional CDF is a function $F:\mathbb R^n\rightarrow\mathbb R$ that can be obtained as some $n$-dimensional probability measure's CDF.
$n$-dimensional CDFs are known to be characterized by certain properties.
 A: For any $x \in \mathbb R^n$, consider the measurable box-like function $M_x(y) = m((-\infty,x]) \cdot \mathbb1_{[x,\infty)}(y)$.  Here $\mathbb1_{[x,\infty)}(y)$ is the indicator function of "$y \ge x$ component-wise".
It should be easy to show that $F(y) = \sup_{x \in \mathbb Q^n} M_x(y)$ for any $y$ (take an increasing sequence of $x$ converging to $y$), and this makes $F$ the countable supremum of measurable functions.
A: Actually the Wikipedia properties are not sufficient. You also need n-monotonicity, which is defined here on pp. 265-266: https://books.google.fi/books?id=GjPUBwAAQBAJ&printsec=frontcover&hl=fi#v=onepage&q&f=false
A more readable treatment is here: http://home.uchicago.edu/hickmanbr/uploads/PT_REVIEW_CH3.pdf
but the monotonicity condition in Theorem 3 is too weak, use the n-monotonicity instead. However, in Theorem 2 the n-monotonicity is correctly stated for R^2:
F(b, d) − F(b, c) − F(a, d) + F(a, c) ≥ 0 for all a < b, c < d.
Beware: the Springer link it seems to use left-continuous CDFs.
