Distribution of $D_i=X_{(i)}-X_{(i-1)}$ where $X_{(i)}$ is the $i$th order statistic I am given a set of iid random variables $\{X_i\}\sim f(x)$  with cdf $F(X)$ I am asked to find the distribution of the following $D_i=X_{(i)}-X_{(i-1)}$  where $X_{(i)}$ is the $i$th  order statistic and $i\ge2$. My approach  is
$$P(D_i=z)=P(X_{(i)}-X_{(i-1)}=z)=P(X_{(i)}=X_{(i-1)}+z)=\int P(X_{(i)}=y+z|X_{(i-1)}=y)P(X_{(i-1)}=y)dy$$
I know
$$P(X_{(i-1)}=y)=\frac{n!}{(i-2)!(n-i+1)!}f(y)F(y)^{i-2}(1-F(y))^{n-i+1}$$
I was trying to find this probability of $P(X_{(i)}=y+z|X_{(i-1)}=y)$ through counting. Let $X_{(i-1)}=y$ then my probability is (where  I am using $X$ as a the distribution representative)
$$P(X_i=y+z, X_j=y \hspace{3mm}\text{for some} \hspace{3mm}i,j\in\{1...n\},X<y+z \hspace{2mm}\text{for} \hspace{2mm} i-2 \text{obs}, X>y+z \hspace{2mm}\text{for} \hspace{2mm} (n-i) \text{obs})$$
Since there are $\frac{n!}{(i-2)!(n-i)!}$ combinations that will  result in the above scenario and each has a probability of $$f(y)f(y+z)F(y+z)^{i-2}(1-F(y+z))^{n-i}$$
then
$$P(D_i=z)=\int\frac{n!}{(i-2)!(n-i)!}f(y)f(y+z)F(y+z)^{i-2}(1-F(y+z)^{n-i}\frac{n!}{(i-2)!(n-i+1)!}f(y)F(y)^{i-2}(1-F(y))^{n-i+1}dy$$
I am having trouble reducing this large integral. So I am guessing if my approach is even correct or if there is a simpler approach?
I am trying to find the joint distribution of $P(D_i=d_i,...,D_n=d_n)$  which basically reduces to $P(X_{(n)}-X_{(1)}=\sum_{i=2}^{n}d_i)$ that is why I need the above distribution.
 A: Yes, indeed, you are close.

The basis for evaluating the probability density function of the event $\{X_{(i)}=y\}$ is that it's outcomes consist of some arrangement of $(i-1)$ samples that are below $y$, one sample that is equal to $y$, and $(n-1)$ samples that are above $y$.  Since we may safely ignore ties as being so to near impossible, there are $n!/(i-1)!(n-i)!$ ways to arrange outcomes that satisfy this.$$f_{\small X_{(i)}}(y)=\dfrac{n! F_{\small X}(y)^{i-1} f_{\small X}(y)(1-F_{\small X}(y))^{n-1}}{(i-1)!1!(n-i)!}$$

Likewise, for $i\in\{2..n\}$, the event of $\{X_{(i-1)}=x,X_{(i)}=x+z\}$ is the event that $i-2$ samples are below $x$, one sample exactly $x$, one sample exactly $x+z$, and the remaining $n-i$ samples are above $x+z$.   Ignoring the zero density posibilities of ties, there are $n!/(i-2)!(n-i)!$ ways to arrange samples that satisfy this criteria.
So then considering that the event of $\{D_i=z\}$ is $\bigcup_x\{X_{(i-1)}=x,X_{(i)}=x+z\}$, we integrate the density over all real values for $x$.
$$\begin{align}f_{\small D_i}(z)&=\int_\Bbb R f_{\small X_{(i-1)},X_{(i)}}(x,x+z)~\mathrm d x\\&=\dfrac{n!}{(i-2)!1!1!(n-i)!}\int_{\Bbb R} F_{\small X}(x)^{i-2}\, f_{\small X}(x)\, f_{\small X}(x+z)\, (1-F_{\small X}(x+z))^{n-i}\,\mathrm d x\end{align}$$
