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Multicollinearity refers to a situation in multiple regression analysis where two or more explanatory (independent) variables are highly linearly correlated. In other words, one explanatory variable can be linearly predicted from the others with a substantial degree of accuracy. This high degree of intercorrelation makes it difficult to isolate the individual effect of each variable on the dependent variable. In econometric models, the basic assumption is that the explanatory variables are linearly independent. When this assumption is violated, the estimates obtained through Ordinary Least Squares (OLS) become unstable and unreliable. Although the OLS estimators still remain unbiased and consistent in the presence of multicollinearity, they become highly sensitive to small changes in the data, and their variances become very large. The major consequence of multicollinearity is _______ _________ _________ ____ _______ ___ __________ ___ ________ _____.
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