This minimization leads to the following estimators of the parameters of the model: The OLS method corresponds to minimizing the sum of square differences between the observed and predicted values. In the case where there are n observations, the estimation of the predicted value of the dependent variable Y for the i th observation is given by: Where Y is the dependent variable, β 0, is the intercept of the model, X j corresponds to the j th explanatory variable of the model (j= 1 to p), and e is the random error with expectation 0 and variance σ². In the case of a model with p explanatory variables, the OLS regression model writes: Equations for the Ordinary Least Squares regression Maximum likelihood and Generalized method of moments estimator are alternative approaches to OLS. Least squares stands for the minimum squares error (SSE). Ordinary Least Squares regression ( OLS) is a common technique for estimating coefficients of linear regression equations which describe the relationship between one or more independent variables and a dependent variable (simple or multiple linear regression).
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |