OLS estimation involves minimizing the sum of squared differences between observed and predicted values from "summary" of Introduction to Econometrics by Christopher Dougherty
The Ordinary Least Squares (OLS) estimation method aims to find the best-fitting line through a set of data points by minimizing the sum of squared differences between the observed values and the values predicted by the estimated line. This process involves finding the coefficients of the regression model that minimize the sum of the squared residuals, or errors, in the model. The residuals are the differences between the observed values and the values predicted by the regression model. By squaring these residuals and summing them up, OLS ensures that both positive and negative errors are taken into account and that larger errors are penalized more than smaller ones. Minimizing the sum of squared residuals is crucial in OLS estimation because it allows us to find the line that best represents the relationship between the independent and dependent variables in the data. This line is the one that minimizes the overall error in predicting the dependent variable based on the independent variable. The OLS method is widely used in econometrics and other fields because of its simplicity and intuitive appeal. By minimizing the sum of squared differences between observed and predicted values, OLS provides a clear and straightforward way to estimate the coefficients of a regression model.- OLS estimation involves finding the coefficients of a regression model that minimize the sum of squared residuals, thereby providing the best-fitting line through a set of data points. This method is essential for understanding and interpreting relationships between variables in empirical research.
