C01F03_2012_10_28

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INTERPRETATION OF A REGRESSION EQUATION

The scatter diagram shows hourly earnings in 2002 plotted against years of schooling, defined as highest grade completed, for a sample of 540 respondents from the National Longitudinal Survey of Youth 1979–.

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Years of schooling (highest grade completed)

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Highest grade completed means just that for elementary and high school. Grades 13, 14, and 15 mean completion of one, two and three years of college.


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Grade 16 means completion of four-year college. Higher grades indicate years of postgraduate education.


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. reg EARNINGS S

Source | SS df MS Number of obs = 540-------------+------------------------------ F( 1, 538) = 112.15 Model | 19321.5589 1 19321.5589 Prob > F = 0.0000 Residual | 92688.6722 538 172.283777 R-squared = 0.1725-------------+------------------------------ Adj R-squared = 0.1710 Total | 112010.231 539 207.811189 Root MSE = 13.126

------------------------------------------------------------------------------ EARNINGS | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- S | 2.455321 .2318512 10.59 0.000 1.999876 2.910765 _cons | -13.93347 3.219851 -4.33 0.000 -20.25849 -7.608444------------------------------------------------------------------------------

This is the output from a regression of earnings on years of schooling, using Stata.

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For the time being, we will be concerned only with the estimates of the parameters. The variables in the regression are listed in the first column and the second column gives the estimates of their coefficients.


. reg EARNINGS S



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In this case there is only one variable, S, and its coefficient is 2.46. _cons, in Stata, refers to the constant. The estimate of the intercept is –13.93.


. reg EARNINGS S



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Here is the scatter diagram again, with the regression line shown.

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EARNINGS = –13.93 + 2.46 S^

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What do the coefficients actually mean?


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EARNINGS = –13.93 + 2.46 S^

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To answer this question, you must refer to the units in which the variables are measured.


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ÊARNINGS = –13.93 + 2.46 S

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S is measured in years (strictly speaking, grades completed), EARNINGS in dollars per hour. So the slope coefficient implies that hourly earnings increase by $2.46 for each extra year of schooling.


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ÊARNINGS = –13.93 + 2.46 S

We will look at a geometrical representation of this interpretation. To do this, we will enlarge the marked section of the scatter diagram.

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ÊARNINGS = –13.93 + 2.46 S

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10.8 11 11.2 11.4 11.6 11.8 12 12.2

Years of schooling

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The regression line indicates that completing 12th grade instead of 11th grade would increase earnings by $2.46, from $13.07 to $15.53, as a general tendency.

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one year

$2.46$13.07

$15.53


You should ask yourself whether this is a plausible figure. If it is implausible, this could be a sign that your model is misspecified in some way.

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ÊARNINGS = –13.93 + 2.46 S

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For low levels of education it might be plausible. But for high levels it would seem to be an underestimate.


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ÊARNINGS = –13.93 + 2.46 S

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What about the constant term? (Try to answer this question yourself before continuing with this sequence.)


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ÊARNINGS = –13.93 + 2.46 S

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Literally, the constant indicates that an individual with no years of education would have to pay $13.93 per hour to be allowed to work.


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ÊARNINGS = –13.93 + 2.46 S

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This does not make any sense at all. In former times craftsmen might require an initial payment when taking on an apprentice, and might pay the apprentice little or nothing for quite a while, but an interpretation of negative payment is impossible to sustain.


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ÊARNINGS = –13.93 + 2.46 S

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A safe solution to the problem is to limit the interpretation to the range of the sample data, and to refuse to extrapolate on the ground that we have no evidence outside the data range.


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ÊARNINGS = –13.93 + 2.46 S

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With this explanation, the only function of the constant term is to enable you to draw the regression line at the correct height on the scatter diagram. It has no meaning of its own.


ÊARNINGS = –13.93 + 2.46 S

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Another solution is to explore the possibility that the true relationship is nonlinear and that we are approximating it with a linear regression. We will soon extend the regression technique to fit nonlinear models.


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ÊARNINGS = –13.93 + 2.46 S

Copyright Christopher Dougherty 2012.

These slideshows may be downloaded by anyone, anywhere for personal use.

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The content of this slideshow comes from Section 1.4 of C. Dougherty,

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

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2012.10.28

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C01F03_2012_10_28