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?

Yes and no. PCA doesn't replace anything with latent variables you might be thinking of factor analysis (FA). PCA does construct principal components (PC) which are linear combinations of all the variables.
If for example you were to find that the first PC explains 90 % of the variability, you could focus on this PC and check which variables mostly contribute to this PC, by checking the loadings/weights/coefficients (which makes sense if your data was normalized). In this case you can find the variables which explain most of the variability... through the PCs.
