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Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: graphing a relationship in a multiple regression model Original citation: Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 3). [Teaching Resource] © 2012 The Author This version available at: http://learningresources.lse.ac.uk/129/ Available in LSE Learning Resources Online: May 2012 This work is licensed under a Creative Commons Attribution-ShareAlike 3.0 License. This license allows the user to remix, tweak, and build upon the work even for commercial purposes, as long as the user credits the author and licenses their new creations under the identical terms. http://creativecommons.org/licenses/by-sa/3.0/ http://learningresources.lse.ac.uk/

Christopher Dougherty EC220 - Introduction to econometrics (chapter 3) Slideshow: graphing a relationship in a multiple regression model Original citation:

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Christopher Dougherty

EC220 - Introduction to econometrics (chapter 3)Slideshow: graphing a relationship in a multiple regression model

Original citation:

Dougherty, C. (2012) EC220 - Introduction to econometrics (chapter 3). [Teaching Resource]

This version available at: http://learningresources.lse.ac.uk/129/

Available in LSE Learning Resources Online: May 2012

http://learningresources.lse.ac.uk/

GRAPHING A RELATIONSHIP IN A MULTIPLE REGRESSION MODEL

. reg EARNINGS S EXP

Source | SS df MS Number of obs = 540-------------+------------------------------ F( 2, 537) = 67.54 Model | 22513.6473 2 11256.8237 Prob > F = 0.0000 Residual | 89496.5838 537 166.660305 R-squared = 0.2010-------------+------------------------------ Adj R-squared = 0.1980 Total | 112010.231 539 207.811189 Root MSE = 12.91

------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.678125 .2336497 11.46 0.000 2.219146 3.137105 EXP | .5624326 .1285136 4.38 0.000 .3099816 .8148837 _cons | -26.48501 4.27251 -6.20 0.000 -34.87789 -18.09213------------------------------------------------------------------------------

The output above shows the result of regressing EARNINGS, hourly earnings in dollars, on S, years of schooling, and EXP, years of work experience.

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EXPSINGSNEAR 56.068.249.26ˆ

GRAPHING A RELATIONSHIP IN A MULTIPLE REGRESSION MODEL

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Suppose that you were particularly interested in the relationship between EARNINGS and S and wished to represent it graphically, using the sample data.

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A simple plot would be misleading.

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Schooling is negatively correlated with work experience. The plot fails to take account of this, and as a consequence the regression line underestimates the impact of schooling on earnings.

. cor S EXP(obs=540) | S ASVABC--------+------------------ S| 1.0000 EXP| -0.2179 1.0000

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. cor S EXP(obs=540) | S ASVABC--------+------------------ S| 1.0000 EXP| -0.2179 1.0000

We will investigate the distortion mathematically when we come to omitted variable bias.

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. cor S EXP(obs=540) | S ASVABC--------+------------------ S| 1.0000 EXP| -0.2179 1.0000

To eliminate the distortion, you purge both EARNINGS and S of their components related to EXP and then draw a scatter diagram using the purged variables.

. reg EARNINGS EXP

Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 2.98 Model | 617.717488 1 617.717488 Prob > F = 0.0847 Residual | 111392.514 538 207.049282 R-squared = 0.0055-------------+------------------------------ Adj R-squared = 0.0037 Total | 112010.231 539 207.811189 Root MSE = 14.389

------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- EXP | .2414715 .1398002 1.73 0.085 -.0331497 .5160927 _cons | 15.55527 2.442468 6.37 0.000 10.75732 20.35321------------------------------------------------------------------------------

. predict EEARN, resid

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We start by regressing EARNINGS on EXP, as shown above. The residuals are the part of EARNINGS which is not related to EXP. The ‘predict’ command is the Stata command for saving the residuals from the most recent regression. We name them EEARN.

. reg S EXP

Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 26.82 Model | 152.160205 1 152.160205 Prob > F = 0.0000 Residual | 3052.82313 538 5.67439243 R-squared = 0.0475-------------+------------------------------ Adj R-squared = 0.0457 Total | 3204.98333 539 5.94616574 Root MSE = 2.3821

------------------------------------------------------------------------------ S | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- EXP | -.1198454 .0231436 -5.18 0.000 -.1653083 -.0743826 _cons | 15.69765 .4043447 38.82 0.000 14.90337 16.49194------------------------------------------------------------------------------

. predict ES, resid

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We do the same with S. We regress it on EXP and save the residuals as ES.

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Now we plot EEARN on ES and the scatter is a faithful representation of the relationship, both in terms of the slope of the trend line (the black line) and in terms of the variation about that line.

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As you would expect, the trend line is steeper that in scatter diagram which did not control for EXP (reproduced here as the red line).

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. reg EEARN ES Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 131.63 Model | 21895.9298 1 21895.9298 Prob > F = 0.0000 Residual | 89496.5833 538 166.350527 R-squared = 0.1966-------------+------------------------------ Adj R-squared = 0.1951 Total | 111392.513 539 206.665145 Root MSE = 12.898------------------------------------------------------------------------------ EEARN | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- ES | 2.678125 .2334325 11.47 0.000 2.219574 3.136676 _cons | 8.10e-09 .5550284 0.00 1.000 -1.090288 1.090288------------------------------------------------------------------------------

From multiple regression:

. reg EARNINGS S EXP------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.678125 .2336497 11.46 0.000 2.219146 3.137105 EXP | .5624326 .1285136 4.38 0.000 .3099816 .8148837 _cons | -26.48501 4.27251 -6.20 0.000 -34.87789 -18.09213------------------------------------------------------------------------------

GRAPHING A RELATIONSHIP IN A MULTIPLE REGRESSION MODEL

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Here is the regression of EEARN on ES.

. reg EEARN ES Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 131.63 Model | 21895.9298 1 21895.9298 Prob > F = 0.0000 Residual | 89496.5833 538 166.350527 R-squared = 0.1966-------------+------------------------------ Adj R-squared = 0.1951 Total | 111392.513 539 206.665145 Root MSE = 12.898------------------------------------------------------------------------------ EEARN | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- ES | 2.678125 .2334325 11.47 0.000 2.219574 3.136676 _cons | 8.10e-09 .5550284 0.00 1.000 -1.090288 1.090288------------------------------------------------------------------------------

From multiple regression:

. reg EARNINGS S EXP------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.678125 .2336497 11.46 0.000 2.219146 3.137105 EXP | .5624326 .1285136 4.38 0.000 .3099816 .8148837 _cons | -26.48501 4.27251 -6.20 0.000 -34.87789 -18.09213------------------------------------------------------------------------------

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A mathematical proof that the technique works requires matrix algebra. We will content ourselves by verifying that the estimate of the slope coefficient is the same as in the multiple regression.

. reg EEARN ES Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 131.63 Model | 21895.9298 1 21895.9298 Prob > F = 0.0000 Residual | 89496.5833 538 166.350527 R-squared = 0.1966-------------+------------------------------ Adj R-squared = 0.1951 Total | 111392.513 539 206.665145 Root MSE = 12.898------------------------------------------------------------------------------ EEARN | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- ES | 2.678125 .2334325 11.47 0.000 2.219574 3.136676 _cons | 8.10e-09 .5550284 0.00 1.000 -1.090288 1.090288------------------------------------------------------------------------------

From multiple regression:

. reg EARNINGS S EXP------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.678125 .2336497 11.46 0.000 2.219146 3.137105 EXP | .5624326 .1285136 4.38 0.000 .3099816 .8148837 _cons | -26.48501 4.27251 -6.20 0.000 -34.87789 -18.09213------------------------------------------------------------------------------

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Finally, a small and not very important technical point. You may have noticed that the standard error and t statistic do not quite match. The reason for this is that the number of degrees of freedom is overstated by 1 in the residuals regression.

. reg EEARN ES Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 131.63 Model | 21895.9298 1 21895.9298 Prob > F = 0.0000 Residual | 89496.5833 538 166.350527 R-squared = 0.1966-------------+------------------------------ Adj R-squared = 0.1951 Total | 111392.513 539 206.665145 Root MSE = 12.898------------------------------------------------------------------------------ EEARN | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- ES | 2.678125 .2334325 11.47 0.000 2.219574 3.136676 _cons | 8.10e-09 .5550284 0.00 1.000 -1.090288 1.090288------------------------------------------------------------------------------

From multiple regression:

. reg EARNINGS S EXP------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.678125 .2336497 11.46 0.000 2.219146 3.137105 EXP | .5624326 .1285136 4.38 0.000 .3099816 .8148837 _cons | -26.48501 4.27251 -6.20 0.000 -34.87789 -18.09213------------------------------------------------------------------------------

GRAPHING A RELATIONSHIP IN A MULTIPLE REGRESSION MODEL

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That regression has not made allowance for the fact that we have already used up 1 degree of freedom in removing EXP from the model.

Subject to respect for copyright and, where appropriate, attribution, they may be

used as a resource for teaching an econometrics course. There is no need to

refer to the author.

The content of this slideshow comes from Section 3.2 of C. Dougherty,

Introduction to Econometrics, fourth edition 2011, Oxford University Press.

Additional (free) resources for both students and instructors may be

http://www.oup.com/uk/orc/bin/9780199567089/.

Individuals studying econometrics on their own and who feel that they might

benefit from participation in a formal course should consider the London School

of Economics summer school course

EC212 Introduction to Econometrics

http://www2.lse.ac.uk/study/summerSchools/summerSchool/Home.aspx

or the University of London International Programmes distance learning course

20 Elements of Econometrics

www.londoninternational.ac.uk/lse.

11.07.25