Financial Risk Manager Part 1

The assumption of no perfect multicollinearity is indeed a fundamental assumption in multiple least squares regression. Multicollinearity refers to a situation where two or more independent variables in a regression model are highly linearly related. In the case of perfect multicollinearity, the independent variables are perfectly linearly related, meaning that one can be expressed exactly as a linear combination of the others. This poses a problem as it undermines the statistical significance of an independent variable. While multicollinearity can inflate the variance of the regression coefficients, making them unstable and difficult to interpret, perfect multicollinearity makes it impossible to estimate the regression coefficients using ordinary least squares. Therefore, the assumption of no perfect multicollinearity is crucial in multiple regression analysis to ensure that the model can provide meaningful and interpretable results.

Why other options are incorrect:

• Choice A: The assumption of homoskedasticity applies to the error terms, not the independent variables in a multiple least squares regression model. Homoskedasticity means that the variance of the error terms is constant across all levels of the independent variables.
• Choice B: In fact, it's quite opposite to one of the key assumptions in multiple least squares regression model which assumes that residuals are homoskedastic, not heteroskedastic. Heteroskedasticity refers to a situation where there are sub-populations that have different variabilities from others.
• Choice C: The assumption about dependent variable being unique and stationary does not apply in multiple least squares regression model. The dependent variable need not be stationary; it can change over time or with changes in other variables.