Econometrics
Ordinary Least Squares
A standard method for estimating the unknown parameters in a linear regression model.
What is Ordinary Least Squares?
Ordinary Least Squares (OLS) finds the line of best fit through a dataset by minimizing the sum of the squared residuals (the differences between the observed values and the values predicted by the model). Under certain assumptions (the Gauss-Markov theorem), OLS provides the most efficient, unbiased estimates of linear relationships.
Why Ordinary Least Squares Matters
It is the fundamental estimation technique underlying most basic regression analysis, favored for its simplicity, mathematical elegance, and ease of interpretation.
Example
A researcher studying the gender pay gap uses OLS to estimate the linear relationship between years of experience and salary, controlling for education level.
Common Mistakes
- Blindly trusting OLS estimates when key assumptions (like homoscedasticity or independent errors) are violated.
- Assuming OLS regression proves causation between the independent and dependent variables.