I don't completely understand the concept of PCA analysis First of all, PCA analysis is not something I came across in my economics studies. But, recently, I wanted to make a PCA analysis of American GDP.
I started to read about the fundamentals of PCA and played around with it in R. Now, I have finally produced a result that can make one period ahead forecast.
I thought: "Great - now I could use the PCA analysis and find the variables that explain most of the variation in GDP and sort them out. I want to know which variables have the most influence". For example: Does consumer spending explain more of the variation in GDP than, let's say, imports of goods and services.
But then I recalled: As far as I understand, the PCA analysis takes the original variables and replaces them with latent components. So my thought is more a matter of correlation - not the PCA analysis itself.
Is this problem more suited for a partial least squares regression PLS analysis?

You could answer your boss's question in focusing on the PCs that best explain the variance of your dataset (as suggested above).
Then, you could decompose those PCs in combinations of your initial variables. The coefficients in front of the variables will tell you how strongly they influence the variability.
