Perhaps I am not saying what I mean as well I as should. What I meant wrt estimations is that independent variable are examined one at a time to decide for selection within a model. If one is looking at investment one would first look at interest rates then add expected income or some other variable which would explain changes not "explained" by the first two. I am quite aware that a modern model is very complex and has many variables.
I did not mean to imply or state that one variable could explain all or most of the changes in a dependent variable.
There was no need to demonstrate the effect of a spurious variable it is understood. It changes nothing that I said.
Not really. Most econometrics seeks to test a given economic theory, and almost always theories involve several variables.
What you seem to be describing is known as "data mining" or "data snooping," i.e. creating an ad hoc model with just as many variables as necessary to "explain" a given phenomenon. Such a practice is generally frowned upon within the profession, as it leads to all kinds of spurious inferences.
If one is looking at investment one would first look at interest rates then add expected income or some other variable which would explain changes not "explained" by the first two.
You're not going to explain investment with single equation model that has interest rates as an independent variable. That's becaue there is significant reverse causality going on, i.e. investment affects interest rates just as much as interest rates affect investment. This is known in the econometric literature as "simultaneity bias" or "endogenetity bias." The upshot is that you have to estimate a multi-equation model.