ie_Slide02

ie_Slide02 - Introductory Econometrics ECON2206/ECON3209...

Info iconThis preview shows pages 1–7. Sign up to view the full content.

View Full Document Right Arrow Icon
Introductory Econometrics ECON2206/ECON3209 Slides02 Lecturer: Rachida Ouysse ie_Slides02 R. Ouysse, School of Economics, UNSW 1
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2. Simple Regression Model (Ch2) 2. Simple Regression Model • Lecture plan otivation and definitions Motivation and definitions – ZCM assumption stimation method: OLS – Estimation method: OLS – Units of measurement onlinear relationships – Nonlinear relationships – Underlying assumptions of simple regression model xpected values and variances of OLS estimators – Expected values and variances of OLS estimators – Regression with STATA ie_Slides02 R. Ouysse, School of Economics, UNSW 2
Background image of page 2
2. Simple Regression Model (Ch2) • Motivation – Example 1. Ceteris paribus effect of fertiliser on soybean yield yield = β 0 + β 1 ferti + u . – Example 2. Ceteris paribus effect of education on wage wage = β 0 + β 1 educ + u . – In general, y = β 0 + β 1 x + u, where u represents factors other than x that affect y . p – We are interested in •explaining y in terms of x , • how y responds to changes in x, holding other factors fixed. ie_Slides02 R. Ouysse, School of Economics, UNSW 3
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2. Simple Regression Model (Ch2) Simple regression model – Definition = β β +u y = 0 + 1 x + u , y : dependent variable (observable) independent variable (observable) x : β 1 : slope parameter, “ partial effect ,” (to be estimated) β 0 : intercept parameter (to be estimated) u : error term or disturbance (unobservable) – The disturbance u represents all factors other than x . – With the intercept β 0 , the population average of u can always be set to zero (without losing anything) = = + E + β + E E ( u ) 0 . y β 0 E ( u ) 1 x u E ( u ) ie_Slides02 R. Ouysse, School of Economics, UNSW 4
Background image of page 4
2. Simple Regression Model (Ch2) = β + β + u • Zero conditional mean assumption – If other factors in u are held fixed ( Δ u = 0), the ceteris y 0 1 x u y + Δ y = β 0 + β 1 ( x+ Δ x ) + u + Δ u paribus effect of x on y is β 1 : Δ y = β 1 Δ x . Δ = “change” – But under what condition u can be held fixed while x changes? •As x and u are treated as random variables, u is fixed while x varying ” is described as the mean of u for any given x is the same (zero) ”. – The required condition is X = X 1 X 2 X 3 ... E ( u | x ) = E ( u ) = 0 , known as zero-conditional-mean (ZCM) assumption. E(u |X) = 0 0 0 0 ie_Slides02 R. Ouysse, School of Economics, UNSW 5
Background image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
2. Simple Regression Model (Ch2) • Zero conditional mean assumption – Example 2. wage = β 0 + β 1 educ + u Suppose u represents ability . Then ZCM assumption amounts to E ( ability | educ ) = 0 , ie, the average ability is the same irrespective of the years of education. This is not true • if people choose the education level to suit their ability; • or if more ability is associated with less (or more) education. In practice, we do not know if ZCM holds and have to eal with this issue deal with this issue.
Background image of page 6
Image of page 7
This is the end of the preview. Sign up to access the rest of the document.

Page1 / 30

ie_Slide02 - Introductory Econometrics ECON2206/ECON3209...

This preview shows document pages 1 - 7. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online