OLS_Wage_Equations_Lectures_FinalInstallment_2perpage

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Unformatted text preview: Econ 1002: Applied Economics Matthew Wakefield University College London and Institute for Fiscal Studies m.wakefield (at) ucl.ac.uk Jan - Mar 2009 Oce Hour: Tues 3-3.55, Room 209, Drayton www.ucl.ac.uk/economics/degree-courses/undergraduate/course-list/courses/econ1002 coursepage or link from: www.ucl.ac.uk/economics/degree-courses/undergraduate/course-list/courses/applied-economics Thanks to the previous two lecturers, Professors Ian Preston and James Banks, for much of the content of this course. Econ 1002 Matthew Wakefield Outline Example Tenure Age Full spec Lessons Outline II: An Introduction to Ordinary Least Squares (OLS) Regression Using the example of Wage Equations 1 Example Wage Equation Job Tenure as omitted variable Non-constant age effects Fullest Specification Lessons from Example Matthew Wakefield (UCL/IFS) Econ 1002 Jan - Mar 2009 2 / 11 Econ 1002 Matthew Wakefield Outline Example Tenure Age Full spec Lessons Example We have estimated: ln w i = + e i + u i Result d ln w i = 0 . 894 + 0 . 0658 e i R 2 = 0.123 Regression explains about 12% of variation in log wage s.e( ) = 0.0041 t-stat = 0.0658 / 0.0041 = 16 So significant(ly different from 0) 95% conf interval: 0.058,0.074 We think there is 95% chance that true is in this interval Matthew Wakefield (UCL/IFS) Econ 1002 Jan - Mar 2009 3 / 11 Econ 1002 Matthew Wakefield Outline Example Tenure Age Full spec Lessons Example Tenure Job tenure +vely correlated w log wage, -vely correlated w edu Omission should bias coeff on edu DOWN...
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