Assumption 2 of the CLRM is necessary but not sufficient to prove that $\hat { \beta } _ { 1 }$ is unbiased.

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Assumption 2 of the Classical Linear Regression Model (CLRM), which states that the explanatory variable is not perfectly collinear, is necessary to ensure that the OLS estimator can be computed. However, to prove that $\hat { \beta } _ { 1 }$ is unbiased, we also need other assumptions such as the expectation of the error term being zero conditional on the explanatory variables.

Assumption 2 of the CLRM Is Necessary but Not Sufficient β^1\hat { \beta } _ { 1 }β^​1​

Assumption 2 of the CLRM Is Necessary but Not Sufficient $\hat { \beta } _ { 1 }$