ECOM30001 Lecture Notes - Lecture 2: Approximation Error, Statistical Parameter, Linear Form

• School: University of Melbourne
• Department: Econometrics
• Course: ECOM30001
• Professor: Andrew Clark

Document Summary

Regression model relates dependant variable (y) to a number of explanatory variables (x = x1, x2 xk) through the linear equation: the parameters are linear, not necessarily the variables, betas are linearly related to explanatory variables (x) T h e e r r o r t e r m ( ) r e p r e s e n t s : Knowledge of all variables that affect y might not be sufficient to perfectly predict y. Represents the average value of y when all the x"s are zero. Represents the expected change in y associated with a unit change in xj, all else constant. Problem we"ve group all these other factors into the error term ( ) To make these statements we need to make assumptions about the error term and the relationship between and the x"s. A s s u m p t i o n 1 e [ | x ] = 0.