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Sometimes, the pattern of coefficients on the year dummy variables is itself of interest. For example, a demographer may be interested in the following question: After controlling for education, has the pattern of fertility among women over age 35 changed between 1972 and 1984? The following table illustrates how this question is simply answered by using multiple regression analysis with year dummy variables.  The table below presents the results from a study carried out on the total number of kids born to a woman. The factors that are controlled for are years of education, age, race, region of the country where living at age 16, and living environment at age 16.  Interpret the results of the estimated model provided above.  and why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)?

Sometimes, the pattern of coefficients on the year dummy variables is itself of interest. For example, a demographer may be interested in the following question: After controlling for education, has the pattern of fertility among women over age 35 changed between 1972 and 1984? The following table illustrates how this question is simply answered by using multiple regression analysis with year dummy variables.  The table below presents the results from a study carried out on the total number of kids born to a woman. The factors that are controlled for are years of education, age, race, region of the country where living at age 16, and living environment at age 16.  Interpret the results of the estimated model provided above.  and why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)?

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
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Sometimes, the pattern of coefficients on the year dummy variables is itself of interest. For example, a demographer may be interested in the following question: After controlling for education, has the pattern of fertility among women over age 35 changed between 1972 and 1984? The following table illustrates how this question is simply answered by using multiple regression analysis with year dummy variables. 

The table below presents the results from a study carried out on the total number of kids born to a woman. The factors that are controlled for are years of education, age, race, region of the country where living at age 16, and living environment at age 16. 

Interpret the results of the estimated model provided above. 

and why can we not use first differences when we have independent cross sections in two years (as opposed to panel data)? 
educ age age² black east northcen west farm rural town smcity y74 y76 y78 y80 y82 Ⓒ The Independent Institute of Education (Pty) Ltd 2022 y84 constant n Independent Variable R² Dependent Variable: Kids Adjusted R² Coefficients -0.128 0.532 -0.006 1.076 0.217 0.363 0.198 -0.053 -0.163 0.084 0.212 0.268 -0.097 -0.069 -0.071 -0.522 -0.545 -7.742 1129 0.1295 0.1162 Standard Errors 0.018 0.138 0.002 0.174 0.133 0.121 0.167 0.147 0.175 0.124 0.160 0.173 Page 4 of 6 2022 0.179 0.182 0.183 0.172 0.175 3.052
Transcribed Image Text:educ age age² black east northcen west farm rural town smcity y74 y76 y78 y80 y82 Ⓒ The Independent Institute of Education (Pty) Ltd 2022 y84 constant n Independent Variable R² Dependent Variable: Kids Adjusted R² Coefficients -0.128 0.532 -0.006 1.076 0.217 0.363 0.198 -0.053 -0.163 0.084 0.212 0.268 -0.097 -0.069 -0.071 -0.522 -0.545 -7.742 1129 0.1295 0.1162 Standard Errors 0.018 0.138 0.002 0.174 0.133 0.121 0.167 0.147 0.175 0.124 0.160 0.173 Page 4 of 6 2022 0.179 0.182 0.183 0.172 0.175 3.052
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