Classical linear Regression model Assumptions | CLRM | Auto | Multi | Hetero | Explained

In this video, I explain the fundamental assumptions of the Classical Linear Regression Model (CLRM), which are essential for ensuring the validity of regression analysis in econometrics. Understanding these assumptions is crucial for accurate model estimation and interpretation. I will cover the following key points: The assumptions behind Ordinary Least Squares (OLS) regression Why each assumption matters in ensuring unbiased, efficient, and consistent estimators Common violations of these assumptions and how they affect your results How to check if these assumptions hold in your data 🔍 What You Will Learn: The Gauss-Markov Theorem and its importance The significance of assumptions like homoscedasticity, no autocorrelation, and normality of errors Practical tips for testing and diagnosing assumption violations Real-world examples of how these assumptions influence regression results