I've never had the pleasure of being a student of Professor Goldberger. We used however his wonderful texbook at UW-Madison, and I've had the opportunity to benefit from his insights during our econometrics workshops and to talk to him a few times about my own research.
I've never met anyone that didn't like him as a person and as a teacher. He was the most intuitive and yet analytically precise econometrician that I've ever met.
As a homage to Professor Goldberger I'll reproduce below his definition of micronumerosity, a tongue-in-cheek concept that he used to raise awareness of common misunderstandings regarding the problem of multicollinearity (see also Bryan Caplan on the topic in this post):
Econometrics texts devote many pages to the problem of multicollinearity in multiple regression, but they say little about the closely analogous problem of small sample size in estimating a univariate mean. Perhaps that imbalance is attributable to the lack of an exotic polysyllabic name for "small sample size." If so, we can remove that impediment by introducing the term "micronumerosity." ...
If micronumerosity proves serious in the sense that the estimate of [the mean] has an unsatisfactorily low degree of precision, we are in the statistical position of not being able to make bricks without straw. The remedy lies essentially in the acquisition, if possible, of larger samples from the same population.
But more data is no remedy for micronumerosity if the additional data are simply "more of the same." So obtaining lots of small samples from the same population will not help. ...
Multicollinearity is no more (or less) serious than micronumerosity. Exact multicollinearity (R2 = 1) is a close analogue of exact micronumerosity (n=0).