unbiased OLS

Unbiased OLS (Ordinary Least Squares) is a statistical method used in regression analysis to estimate the parameters of a linear regression model. In simple terms, it is a technique to find the line of best fit for a given set of data points.

In an OLS regression model, the goal is to find the values of the coefficients (slope and intercept) that minimize the sum of the squared differences between the predicted values and the actual values of the dependent variable.

Unbiased OLS refers to a specific type of OLS regression where the estimated coefficients are unbiased, meaning that the expected value of the coefficients equals the true values of the population coefficients. This is important because if the estimated coefficients are biased, the predictions made by the model will also be biased, leading to inaccurate results.

To ensure unbiasedness in OLS regression, certain assumptions must be met, such as normality of the errors, homoscedasticity, and independence of observations. If these assumptions are not met, alternative regression methods may need to be used.