slides7 - ECON 103 Lecture 7 Multiple regression model...

Info icon This preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
ECON 103, Lecture 7: Multiple regression model Maria Casanova April 21st (version 2) Maria Casanova Lecture 7
Image of page 1

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

View Full Document Right Arrow Icon
Requirements for this lecture: Chapter 4 and chapter 6 of Stock and Watson Maria Casanova Lecture 7
Image of page 2
0. Introduction Remember the example in lecture 6 where we modeled the relationship between wages ( Y ) and fitness ( X 1 ) using a univariate regression model: Y i = β 0 + β 1 X 1 i + ε i By restricting our attention to the relationship between Y and X 1 we ignored some other potentially important determinants of wages such as age ( X 2 ). Omitting potentially relevant regressors can lead to an incorrect estimate of the population regression line (i.e. cause a bias in the OLS estimator ˆ β 1 ) in the presence of two conditions: 1 the omitted regressor ( X 2 ) is correlated with the regressor X 1 . 2 the omitted regressor ( X 2 ) affects the dependent variable Y . Maria Casanova Lecture 7
Image of page 3

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

View Full Document Right Arrow Icon
0. Introduction Figure: X 1 (fitness) is uncorrelated with X 2 (age) 0 1 2 3 4 5 6 7 8 9 10 0 500 1000 1500 fitness wage (highest) (lowest) X 1 = β 0 = 1000 β 1 = 0 Maria Casanova Lecture 7
Image of page 4
0. Introduction Figure: X 1 (fitness) is correlated with X 2 (age) 0 1 2 3 4 5 6 7 8 9 10 0 500 1000 1500 fitness wage (highest) (lowest) X 1 = β 0 = 1000 β 1 = 0 Maria Casanova Lecture 7
Image of page 5

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

View Full Document Right Arrow Icon
0. Introduction Under conditions (1) and (2), the OLS estimator will have omitted variable bias , which means that the first least squares assumption does not hold, i.e. E ( ε i | X i ) 6 = 0 How do we address omitted variable bias? we can divide the data into smaller groups (e.g. run separate regressions of wage on fitness for individuals aged 20-25, 25-30, etc.) This can become unpractical as we add more regressors. Moreover, this estimate does not provide an overall measure of the effect on wages of increasing fitness holding age constant . The estimate of the effect on wages of changing fitness holding age constant can be obtained using the multiple regression model . Maria Casanova Lecture 7
Image of page 6
1. Multiple regression model In the multiple regression model more than one variable affects the dependent variable.
Image of page 7

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

View Full Document Right Arrow Icon
Image of page 8
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern