What is meant by multicollinearity? What are its consequences on estimates? What remedial measures do you suggest for the problem?

Multicollinearity refers to a situation in econometrics where two or more independent variables in a regression model exhibit a high degree of correlation. In simpler terms, when explanatory variables are highly interrelated, it becomes difficult to assess the individual effect of each variable on the dependent variable. Multicollinearity does not violate the basic assumptions of the classical linear regression model (CLRM), but it can lead to problems in estimating reliable and interpretable coefficients.

Multicollinearity can be classified into two types: perfect multicollinearity and imperfect multicollinearity. Perfect multicollinearity occurs when an independent variable is an exact linear combination of one or more other independent variables. In such cases, the regression model cannot be estimated because the design matrix becomes singular. Imperfect multicollinearity, on the other hand, occurs when independent variables are highly, but not perfectly, correlated, which can still cause estimation issues.

The primary consequences of multicollinearity are as follows:

1. Inflated Standard Errors of Estimates – When multicollinearity exists, the variance of estimated coefficients increases, making the estimates less precise. This results _______ __________ ______ ______ _______ _________ _____ _____ _____ ______.
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