EC295 Lecture Notes - Lecture 3: Linear Combination, Ceteris Paribus, Econometrics
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Simple regression analysis: to explain a dependent variable y, as a function of a single independent variable x. Difficult to draw ceteris paribus conclusions about how x affects y. Multiple regression analysis: follows ceteris paribus: allows you to control for many other factors that simultaneously affect the dependent variable, test variables that may be correlated. Build better models for predicting the dependent variable: more flexibility-more variables. Estimate parameters in the multiple regression model using the method of ordinary least squares. Contains two functions of income, inc and inc2 (and therefore three parameters, b0 ,b1 and b2 ). Each equation can be written as (3. 3) Other factors affecting wage are not related on average to educ and exper. If we think ability is part of u, then we will need average ability levels to be the same across all combinations of education and experience in the working population. Allows many observed factors to affect y.
