Critically evaluate the following statements:

a. "In fact, multicollinearity is not a modeling error. It is a condition of de?cient data."*

b. "If it is not feasible to obtain more data, then one must accept the fact that the data one has contain a limited amount of information and must simplify the model accordingly. Trying to estimate models that are too complicated is one of the most common mistakes among in- experienced applied econometricians."

c. "It is common for researchers to claim that multicollinearity is at work whenever their hypothesized signs are not found in the regres- sion results, when variables that they know a priori to be important have insigni?cant t values, or when various regression results are changed substantively whenever an explanatory variable is deleted. Unfortunately, none of these conditions is either necessary or suf?- cient for the existence of collinearity, and furthermore none provides any useful suggestions as to what kind of extra information might be required to solve the estimation problem they present."‡

d. ". . . any time series regression containing more than four indepen- dent variables results in garbage."*