Linear Regression Model: Properties and Estimation

The objectives of any regression analysis are to estimate the unknown parameters in the model, to validate whether the functional form of the model is consistent with the hypothesised model based on the theory, and to use the model to predict future values of the response variable. This chapter discusses linear regression model and its application with cross section data. Linear regression is a method of estimating the conditional expected value of the response or dependent variable given the values of a set of predictor or independent variables. Multiple regression analysis is more amenable to ceteris paribus analysis because it allows us to explicitly control for many other factors which simultaneously affect the dependent variable. The power of multiple regression analysis is that it provides the ceteris paribus interpretation even though the data have not been collected in a ceteris paribus fashion.