Take $b\in\mathbb{R}$. Suppose $X_n\to X$ in distribution and suppose $Y_n\to y$ in probability, where $y$ is a constant. How to prove $$\Bbb P(Xy\leq b) \leq \liminf_{n\to\infty}\Bbb P(X_nY_n\leq b)?$$
I've asked the question before but I didn't get any answer. So I tried myself and hopefully someone can follow my reasoning.
Note: I know that this follows from Slutsky's theorem, but I'm trying to use this question to prove Slutsky's theorem.
My try: Since $X_n\to X$ in distribution, then given $\epsilon>0$ there an $n\in\mathbb{N}$ such that for all $n\geq N$
$$\Bbb P(Xy\leq b)\leq\epsilon+\Bbb P(X_ny\leq b),\;\;\text{ and so }\;\;\; \Bbb P(Xy\leq b)\leq\epsilon+\inf_{n\geq N}\Bbb P(X_ny\leq b).$$
Now we need to use the fact that $Y_n\to y$ in probability.
We can write the set
$$\{X_ny\leq b\}=\{X_ny\leq b,Y_n\in (y-\epsilon,y+\epsilon) \}\cup \{X_ny\leq b,Y_n\not\in (y-\epsilon,y+\epsilon) \},$$
and so $$
\{X_ny\leq b\}\subseteq\underbrace{\{X_nY_n\leq b+\epsilon}_{:=A_n} \}\cup \underbrace{\{Y_n\not\in (y-\epsilon,y+\epsilon) \}}_{:=B_n},$$
Now we know that $\liminf_{n\to\infty}\Bbb P(B_n)=0$. Now let's look at $A_n$. For the sake of simplicity let's assume $X_n>0$.
Then we have that
$$A_n=\{X_nY_n\leq b\}\cup \underbrace{\{b<X_nY_n\leq b+\epsilon\}}_{C_n}.$$
In total, we have that
$$\Bbb P(X_ny\leq b)\leq \Bbb P(B_n) + \Bbb P(X_nY_n\leq b)+\mathbb{P}(C_n) $$
and
$$ \Bbb P(Xy\leq b)\leq \Bbb P(X_nY_n\leq b)+\epsilon + \Bbb P(B_n) +\mathbb{P}(C_n) $$
and so finally we have made the set $\{X_nY_n\leq b\}$ appear. Taking $\liminf$ we have that
$$ \Bbb P(Xy\leq b)\leq \liminf_{n\to\infty}\Bbb P(X_nY_n\leq b)+\epsilon +\liminf_{n\to\infty}\mathbb{P}(C_n) $$
But then is it possible to bound the probability of $C_n=\{b<X_nY_n\leq b+\epsilon\}$ as a function of $\epsilon$?