Answer: It will be consistent, unbiased, and efficient

In the presence of near multicollinearity, the OLS estimator will still be consistent, unbiased, and efficient. This happens because none of the four Gauss-Markov assumptions of the Classical Linear Regression Model (CLRM) have been violated. **Key Points:** 1. **Near multicollinearity** is defined as a situation where there is a high, but not perfect, correlation between two or more explanatory variables. 2. **Gauss-Markov assumptions** include: - Linearity - Non-collinearity (no perfect multicollinearity) - Exogeneity (zero conditional mean) - Homoscedasticity (constant variance) - No autocorrelation 3. **Near multicollinearity** does not violate the non-collinearity assumption, which only requires that there is no perfect linear relationship between regressors. 4. **Practical implications**: While the OLS estimator remains BLUE (Best Linear Unbiased Estimator) under near multicollinearity, the estimates may have: - High standard errors - Unstable coefficient estimates - Difficulty in interpreting individual coefficients - But the estimator properties (consistency, unbiasedness, efficiency) are preserved. **Note**: There is no definitive threshold for what constitutes "high" correlation in near multicollinearity, but it typically refers to correlation coefficients above 0.7-0.8.

Author: Nikitesh Somanthe