Answer: It will be consistent, unbiased, but not efficient.

## Explanation In the presence of near multicollinearity, the OLS estimator maintains the following properties: - **Consistent**: As the sample size increases, the estimator converges to the true parameter value - **Unbiased**: The expected value of the estimator equals the true parameter value - **Not Efficient**: The variance of the estimator becomes inflated, making it less precise ### Key Points: - Multicollinearity increases the standard errors of coefficient estimates - While the estimates remain unbiased, their precision decreases significantly - High variance inflation factor (VIF) values indicate multicollinearity issues - The estimator still converges to the true parameters with large sample sizes Therefore, option C correctly describes the properties of OLS estimators under near multicollinearity conditions.

Author: Tanishq Prabhu