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# Chapter3 Multi Reg Estim.pdf

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```Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Multiple Regression Model
ECON 371: Econometrics
September 22, 2021
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Motivation for Multiple Regression
The Model with k Independent Variables
Mechanics and Interpretation of OLS
Interpreting the OLS Regression Line
The Expected Value of the OLS Estimators
Inclusion of Irrelevant Variables
Omitted Variable Bias
The Variance of the OLS Estimators
Variances in Misspeficied Models
Estimating the Error Variance
Efficiency of OLS: The Gauss-Markov Theorem
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I Consider an extension of the ln(wage) equation we used for
simple regression:
ln(wage) = β0 + β1 educ + β2 IQ + u
(1)
where IQ is IQ score (in the population, it has a mean of 100
and sd = 15).
I We are primarily interested in β1 , but β2 is of some interest,
too.
I We took IQ out of the error term, explicitly including it in the
equation. If IQ is a good proxy for intelligence, this may lead
to a more persuasive estimate of the causal effect of schooling.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I As another example, what is the relationship between missing
lecture and final exam score?
I In a simple regression approach, we would relate final to
missed (number of lectures missed). But part of the error
term would be a student’s ability.
final = β0 + β1 missed + β2 priGPA + u
(2)
where priGPA is grade point average at the beginning of the
term.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
Generally, we can write a model with two independent variables as
y = β0 + β1 x1 + β2 x2 + u,
where β0 is the intercept, β1 measures the change in y with
respect to x1 , holding other factors fixed, and β2 measures the
change in y with respect to x2 , holding other factors fixed.
ECON 371: Econometrics
The Multiple Regression Model
(3)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
In the model with two independent variables, the key assumption
about how u is related to x1 and x2 is
E (u|x1 , x2 ) = 0.
(4)
I For any values of x1 and x2 in the population, the average
unobservable is equal to zero. (The value zero is not
important because we have an intercept, β0 in the equation.)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I In the wage equation, the assumption is E (u|educ, IQ) = 0.
Now u no longer contains intelligence (we hope), and so this
condition has a better chance of being true. In simple
regression, we had to assume IQ and educ are unrelated to
justify leaving IQ in the error term.
I Other factors, such as workforce experience and ”motivation”,
are part of u. Motivation is very difficult to measure.
Experience is easier:
ln(wage) = β0 + β1 educ + β2 IQ + β3 exper + u.
ECON 371: Econometrics
The Multiple Regression Model
(5)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I The multiple linear regression model can be written in the
population as
y = β0 + β1 x1 + β2 x2 + ... + βk xk + u
(6)
where β0 is the intercept, β1 is the parameter associated with
x1 , β2 is the parameter associated with x2 , and so on.
I Contains k + 1 (unknown) population parameters. We call
β1 , ..., βk the slope parameters.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I Now we have multiple explanatory or independent variables.
We still have one explained or dependent variable and an error
term, u.
I Multiple regression allows more flexible functional forms:
ln(wage) = β0 +β1 educ +β2 IQ +β3 exper +β4 exper 2 +u, (7)
so that exper is allowed to have a quadratic effect on
ln(wage).
I Take x1 = educ, x2 = IQ, x3 = exper, and x4 = exper 2 . Note
that x4 is a nonlinear function of x3 .
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I We already know that 100 &middot; β1 is the(ceteris paribus)
percentage change in wage [not ln(wage)!] when educ
increases by one year. 100 &middot; β2 has a similar interpretation (for
a one point increase in IQ).
I β3 and β4 are harder to interpret, but we can use calculus to
get the slope of lwage with respect to exper :
∂lwage
= β3 + 2β4 exper
∂exper
Multiply by 100 to get the percentage effect.
ECON 371: Econometrics
The Multiple Regression Model
(8)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The Model with k Independent Variables
I The key assumption for the general multiple regression model
is easy to state in terms of a conditional expectation:
E (u|x1 , ..., xk ) = 0
(9)
I Provided we are careful, we can make this condition closer to
being true by ”controlling for” more variables. In the wage
education.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Suppose we have x1 and x2 (k = 2) along with y . We want to fit
an equation of the form
ŷ = β̂0 + β̂1 x1 + β̂2 x2
(10)
given data {(xi1 , xi2 , yi ) : i = 1, ..., n}. The sample size is again n.
Now the explanatory variables have two subscripts: i is the
observation number (as always) and the second subscript (1 and 2
in this case) are labels for particular variables. For example,
xi1 = educi , i = 1, ..., n
(11)
xi2 = IQi , i = 1, ..., n
(12)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I As in the simple regression case, there are different ways to
motivate OLS. We choose β̂0 , β̂1 , and β̂2 (so three unknowns)
to minimize the sum of squared residuals,
n
X
(yi − β̂0 − β̂1 xi1 − β̂2 xi2 )2
(13)
i=1
I The case with k independent variables is easy to state: choose
the k + 1 values β̂0 , β̂1 , β̂2 , ..., β̂k to minimize
n
X
(yi − β̂0 − β̂1 xi1 − ... − β̂k xik )2
i=1
ECON 371: Econometrics
The Multiple Regression Model
(14)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
This can be solved with multivariable calculus. The OLS first
order conditions are the k + 1 linear equations in the k + 1
unknowns β̂0 , β̂1 , β̂2 , ..., β̂k :
n
X
(yi − β̂0 − β̂1 xi1 − ... − β̂k xik ) = 0
(15)
xi1 (yi − β̂0 − β̂1 xi1 − ... − β̂k xik ) = 0
(16)
xi2 (yi − β̂0 − β̂1 xi1 − ... − β̂k xik ) = 0
(17)
..
.
(18)
xik (yi − β̂0 − β̂1 xi1 − ... − β̂k xik ) = 0
(19)
i=1
n
X
i=1
n
X
i=1
n
X
i=1
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I We will discuss the condition needed for this set of equations
to have a unique solution. Computers are good at finding the
solution.
I The OLS regression line is generally written
ŷ = β̂0 + β̂1 x1 + β̂2 x2 + ... + β̂k xk
(20)
I In applications, we will replace y and the xj with real variable
names, and the β̂j with the numbers provided by Stata (or
any other regression package).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I This is important. The slope coefficients now explicitly have
ceteris paribus interpretations.
I Consider k = 2:
ŷ = β̂0 + β̂1 x1 + β̂2 x2
(21)
∆ŷ = β̂1 ∆x1 + β̂2 ∆x2
(22)
Then,
tells us how predicted y changes when x1 and x2 change by
any amount.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
What if we ”hold x2 fixed” ?
∆ŷ = β̂1 ∆x1 if ∆x2 = 0
(23)
∆ŷ = β̂2 ∆x2 if ∆x1 = 0
(24)
Similarly,
We call β̂1 and β̂2 partial effects or ceteris paribus effects.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Terminology
We say that β̂0 , β̂1 , ..., β̂k are the OLS estimates from the
regression
y on x1 , x2 , ..., xk
(25)
yi on xi1 , xi2 , ..., xik , i = 1, ..., n
(26)
or
when we want to emphasize the sample being used.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
EXAMPLE: Final Exam Score and Missed Classes
I Sample of 680 MSU students in Economics 201 (from several
years ago). Attendance was recorded electronically.
I We first regress final (out of 40 points) on missed(lectures
missed out of 32) in a simple regression analysis. Then we
add priGPA (prior GPA) as a control for quality of student.
d = 26.60 − .121 missed
final
(27)
n = 680
(28)
I So the simple regression estimate implies that, say, 10 more
missed classes reduces the predicted score by about 1.2 points
(out of 40, with sd = 4.71).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I Now we control for prior GPA:
d = 17.42 + 0.017 missed + 3.24 priGPA
final
(29)
n = 680
(30)
I The coefficient on missed actually becomes positive, but it is
very small, anyway. (Later, we will see it is not statistically
different from zero.)
I The coefficient on priGPA means that one more point on prior
GPA (for example, from 2.5 to 3.5) predicts a final exam score
that is 3.24 points higher, holding the number of missed class
constant.
I Note that missed and priGPA are negatively correlated,
Corr (missed, priGPA) = −0.427.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Now the wage example:
\ = 5.97 + 0.0598 educ
lwage
(31)
n = 935
(32)
\ = 5.66 + 0.0391 educ + .0058 IQ
lwage
(33)
n = 935
(34)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
6.0% to about 3.9% when we control for IQ.
I In the multiple regression, we do this thought experiment:
take two people, A and B, with the same IQ score. Suppose
person B has one more year of schooling than person A. Then
we predict B to have a wage that is 3.9% higher.
I Simple regression does not allow us to compare people with
the same IQ score. The larger estimated return is because we
are attributing part of the IQ effect to education.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I Not suprisingly, there is a nontrival, positive correlation
between educ and IQ: 0.52.
I Multiple regression ”partials out” the other independent
variables when looking at the effect of, say, educ. It can be
shown that β̂1 measures the effect of educ on lwage once the
correlation of educ and IQ has been netted or partialled out.
I Another IQ point is worth much less than a year of education.
Holding educ fixed, 10 more IQ points increases predicted
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
What Does it Mean to ”Hold Other Factors Fixed?”
I The beauty of multiple regression is that it gives us the ceteris
paribus interpretation without having to find two people with
the same value of IQ who differ in education by one year. The
estimation method does that for us.
I Multiple regression effectively mimics the ability to obtain a
sample of individuals with same, say, IQ and different levels of
education.
I You can see in the first 20 observations that education and IQ
score are free to vary. There are, of course, common values.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Fitted Values and Residuals
ŷi = β̂0 + β̂1 xi1 + β̂2 xi2 + ... + β̂k xik
(35)
ûi = yi − ŷi
(36)
1. The residuals always average to zero,
implies ȳ = ŷ .
Pn
i=1 ûi
= 0. This
2. Each explanatory variable is uncorrelated with the residuals in
the sample. This follows from the first order conditions. It
implies that ŷi and ûi are also uncorrelated.
3. The sample averages always fall on the OLS regression line:
ȳ = β̂0 + β̂1 x̄1 + β̂2 x̄2 + ... + β̂k x̄k
ECON 371: Econometrics
The Multiple Regression Model
(37)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Goodness-of-Fit
As with simple regression, it can be shown that
SST = SSE + SSR
(38)
where SST , SSE , and SSR are the total, explained, and residual
sum of squares.
We define the R-squared as before:
R2 =
SSE
SSR
=1−
SST
SST
ECON 371: Econometrics
The Multiple Regression Model
(39)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I In addition to 0 ≤ R 2 ≤ 1 and the intepretation of R 2 , there
is a useful fact to remember: using the same set of data and
the same dependent variable, the R-squared can never fall
when another independent variable is added to the regression.
And, it almost always goes up, at least a little.
I This means that, if we focus on R 2 , we might include silly
variables among the xj .
I Adding another x cannot make SSR increase. The SSR falls
unless the coefficient on the new variable is identically zero.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I In the missed lectures example, the R 2 from final on missed is
only .0196. When we add priGPA, R 2 = .1342, and priGPA is
doing all the work. If we just regress final on priGPA,
R 2 = .1339, which is barely below when missed is included.
I In the wage example, the R 2 from lwage on educ is 0.0974,
and it increases to 0.1297 when IQ is added.
I Notice that, together – we cannot separate out the
contribution of each, because they are correlated – educ and
IQ explain less than 15% of the variation in lwage. Over 85%
is left unexplained.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Comparing Simple and Multiple Regression Estimates
Consider the simple and multiple OLS regression lines:
ỹ = β̃0 + β̃1 x1
(40)
ŷ = β̂0 + β̂1 x1 + β̂2 x2
(41)
where a tilda (˜) denotes the simple regression and hat (ˆ) denotes
multiple regression (using the same n observations).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I Question: Is there a simple relationship between β̃1 , which
does not control for x2 , and β̂1 , which does?
I Yes, but we need to define another simple regression. Let δ̃1
be the slope from the regression
xi2 on xi1 , i = 1, ..., n
(42)
(Yes, x2 plays the role of the dependent variable.)
I It is always true – that is, an algebraic fact – that
β̃1 = β̂1 + β̂2 δ̃1
ECON 371: Econometrics
The Multiple Regression Model
(43)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Notice how, in β̃1 = β̂1 + β̂2 δ̃1 , the slope coefficient on x2 in the
multiple regression also plays a role.
Case 1: If the partial effect of x2 on y is positive, so β̂2 &gt; 0, and
x1 and x2 are positive correlated in the sample, so δ̃1 &gt; 0, then
β̃1 &gt; β̂1
EXAMPLE: In the lwage example with x1 = educ, x2 = IQ,
β̂2 = 0.00586, δ̃1 = 3.534, and we saw that
β̃1 = 0.0598 &gt; 0.039 = β̂1 .
ECON 371: Econometrics
The Multiple Regression Model
(44)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Case 2: If β̂2 &gt; 0 and δ̃1 &lt; 0 (x1 and x2 negatively correlated), or
vice-versa, then
β̃1 = β̂1 + β̂2 δ̃1 = β̂1 + (+)(−) = β̂1 + (−) &lt; β̂1
(45)
EXAMPLE: In the missed lectures example with x1 = missed and
x2 = priGPA, β̂2 = 3.24, δ̃1 = −0.0427, and we saw that
β̃1 = −0.121 &lt; 0.017 = β̂1 .
I β̃1 = β̂1 only when the partial effect of x2 on y is equal to
zero in the sample(β2 = 0), and/or x1 and x2 are uncorrelated
in the sample (δ̃1 = 0).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
Regression Through the Origin
I Occasionally, one wants to impose that the predicted y is zero
when all the xj are zero. This means the intercept should be
set to zero, rather than estimated:
ỹ = β̃1 x1 + β̃2 x2 + ... + β̃k xk
(46)
I The costs of imposing a zero intercept when the population
intercept is not zero are severe: all of the slope coefficients are
biased, in general.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I Estimating an intercept when β0 = 0 does not cause in bias in
the slope coefficients (under the appropriate assumptions).
SLR.1 to SLR.4 says the population parameters must be
different from zero.
I Also, computing an appropriate R-squared becomes a bit
tricky. In this class, we always estimate the intercept.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I We motivated OLS estimation using the population regression
function
E (y |x1 , x2 , ..., xk ) = β0 + β1 x1 + β2 x2 + ... + βk xk
(47)
I We obtaind the sample regression function,
ŷ = β̂0 + β̂1 x1 + β̂2 x2 + ... + β̂k xk
(48)
by choosing the β̂j to minimize the sum of squared residuals.
I The β̂j have a ceteris paribus interpretation:
ŷ = β̂1 ∆x1 , ∆x2 = ... = ∆xk = 0
ECON 371: Econometrics
The Multiple Regression Model
(49)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Interpreting the OLS Regression Line
I We discussed algebraic properties of fitted values and
residuals. These hold for any sample. Also, features of R 2 ,
the goodness-of-fit measure.
I The only assumption we needed to discuss the algebraic
properties for a given sample is that we can actually compute
the estimates.
I Now we turn to statistical properties and features of the study
the sampling distributions of the estimators. We go further
than in the simple regression case, eventually covering
statistical inference.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I As with simple regression, there is a set of assumptions under
which OLS is unbiased. We also explicitly consider the bias
caused by omitting a variable that appears in the population
model.
I Now we label the assumptions with ”MLR” (multiple linear
regression).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
Assumption MLR.1 (Linear in Parameters)
I The model in the population can be written as
y = β0 + β1 x1 + β2 x2 + ... + βk xk + u
(50)
where the βj are the population parameters and u is the
unobserved error.
I We have seen examples already where y and the xj can be
nonlinear functions of underlying variables, and so the model
is flexible.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
Assumption MLR.2 (Random Sampling)
I We have a random sample of size n from the population,
{(xi1 , xi2 , ..., xik , yi ) : i = 1, ..., n}
I As with SLR, this introduces the data and means the data are
a representative sample from the population.
Sometimes we will plug the observations into the population
equation:
yi = β0 + β1 xi1 + β2 xi2 + ... + βk xik + ui
which emphasizes that, along with the observed variables, we
effectively draw an unobserved error, ui , for each i.
ECON 371: Econometrics
The Multiple Regression Model
(51)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
Assumption MLR.3 (No Perfect Collinearity)
I In the sample (and, therefore, in the population), none of the
explanatory variables is constant, and there are no exact linear
relationships among them.
I Ruling out no sample variation in {xij : i = 1, ..., n} is clear
from simple regression.
I There is a new part to the assumption because we have more
than one explanatory variable. We must rule out the
(extreme) case that one (or more) of the explanatory variables
is an exact linear function of the others.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I If, say, xi1 is an exact linear function of xi2 , ..., xik , we say the
model suffers from perfect collinearity.
I Under perfect collinearity, there are no unique OLS estimators.
Stata and other regression packages will indicate a problem.
I Usually perfect collinearity arises from a bad specification of
the population model. A small sample size or bad luck in
drawing the sample can also be the culprit.
I Assumption MLR.3 can only hold if n ≥ k + 1, that is,we have
at least as many observations as we have parameters to
estimate.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
Suppose that k = 2 and x1 = educ, x2 = exper. If we draw our
sample so that
educi = 2exper i
(52)
for every i, then Assumption MLR.3 is violated. Very unlikely
unless the sample is small. (In the population, there are plenty of
people for which years of schooling is not twice their years of
workforce experience.)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I With the samples we have looked at, the presence of perfect
collinearity usually is a result of poor model specification, or
defining variables inappropriately.
I Such problems can almost always be detected by remembering
the ceteris paribus nature of multiple regression.
I EXAMPLE: Do not include the same variable in an equation
that is measured in different units. For example, in a CEO
salary equation, it would make no sense to include firm sales
measured in dollars along with sales measured in millions of
dollars. There is no new information once we include one of
these.
I The return on equity should be included as a percent or
proportion, not both.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I EXAMPLE: Be careful with functional forms! Suppose we
ln(cons) = β0 + β1 ln(inc) + u
(53)
I How might we allow the elasticity to be nonconstant, but
include the above as a special case? The following does not
work:
ln(cons) = β0 + β1 ln(inc) + β2 ln(inc 2 ) + u
because ln(inc 2 ) = 2 ln(inc), that is, x2 = 2x1 , where
x1 = ln(inc).
ECON 371: Econometrics
The Multiple Regression Model
(54)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I Instead, we really mean something like
ln(cons) = β0 + β1 ln(inc) + β2 [ln(inc)]2 + u
(55)
which means x2 = x12 . So x2 is an exact nonlinear function,
but this (fortunately) is allowed in MLR.3.
I Tracking down perfect collinearity can be harder when it
involves more than two variables.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
EXAMPLE: Motivated by the data in VOTE.DTA:
voteA = β0 + β1 expendA + β2 expendB + β3 totexpend + u (56)
where expendA is campaign spending by candidate A, expendB is
spending by candidate B, and totexpend is total spending. All are
in thousands of dollars. Mechanically, the problem is that, by
definition,
expendA + expendB = totexpend
which, of course, will be true then for any sample we collect.
ECON 371: Econometrics
The Multiple Regression Model
(57)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I One of the three variables has to be dropped. (Stata does this
automatically, but we should rely on ourselves to properly
construct a model and interpret it.)
I The model makes no sense from a ceteris paribus perspective.
For example, β1 is suppose to measure the effect of changing
expendA on voteA, holding fixed expendB and totexpend.
But if expendB and totexpend are held fixed, expendA cannot
change!
I We would probably drop totexpend and just use the two
separate spending variables.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I The results of this regression seem sensible: spending by
candidate A has a positive effect on the share of the vote
received by A, and spending by B has essentially the opposite
effect. (If expendA increases by 10, so \$10,000, and expendB
is held fixed, the voteA is estimated to increase by about .38
percentage points.)
I Note that shareA, which is a nonlinear function of expendA
and expendB,
shareA = 100 &middot;
expendA
(expendA + expendB)
(58)
can be included. It allows for the relative size of spending to
matter.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I A Key Point: Assumption MLR.3 does not say the
explanatory variables have to be uncorrelated. Nor does it say
they cannot be ”highly” correlated. It rules out perfect
correlation, that is, correlations of &plusmn;1. Very rare unless a
mistake has been made in specifying the model.
I Multiple regression would be almost useless if we had to insist
x1 , ..., xk were uncorrelated in the sample (or population)!
I If the xj were all pairwise uncorrelated, we could just use a
bunch of simple regressions.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I In an equation like
lwage = β0 + β1 educ + β2 IQ + β3 exper + u,
(59)
we fully expect correlation among educ, IQ, and exper.
(Already saw educ and IQ are positively correlated; educ and
exper tend to be negatively correlated.)
I If educ were uncorrelated with all other variables that affect
lwage, we could stick with simple regression of lwage on educ
to estimate β1 .
I Multiple regression allows us to estimate ceteris paribus
effects precisely when there is correlation among the xj .
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I MLR.1: y = β0 + β1 x1 + β2 x2 + ... + βk xk + u
I MLR.2: random sampling
I MLR.3: no perfect collinearity
The last assumption ensures that the OLS estimators are unique
and can be obtained from the first order conditions (minizing the
sum of squared residuals).
We need a final assumption for unbiasedness.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
Assumption MLR.4 (Zero Conditional Mean)
E (u|x1 , x2 , ..., xk ) = 0
(60)
I The average value of the error does not change across
different slices of the population defined by x1 , ..., xk .
I If u is correlated with any of the xj , MLR.4 is violated. This is
usually a good way to think about the problem.
I MLR.4 would also be violated if we misspecify the functional
relation between explained and explanatory variables in
MLR.1. For example, if we use wage when the true
relationship in the population has y = ln(wage).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I EXAMPLE:
score = β0 + β1 classize + β2 income + u
(61)
I Even at the same income level, families differ in their interest
and concerns about their children’s education. Family support
and student motivation is in u. Is this correlated with class
size?
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I When Assumption MLR.4 holds, we say x1 , ..., xk are
exogenous explanatory variables. If xj is correlated with u,
we often say xj is an endogenous explanatory variable.
(This name makes more sense in more advanced contexts, but
it is used generally.)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
THEOREM (Unbiasedness of OLS)
I Under Assumptions MLR.1 through MLR.4, the OLS
estimators are unbiased:
E (β̂j ) = βj , j = 0, 1, 2, ..., k
(62)
for any values of the βj .
I The easiest proof requires matrix algebra. See Appendix 3A
for a proof based on summations.
I The hope is that if our focus is on, say, x1 , we can include
enough other variables in x2 , ..., xk to make MLR.4 true, or
close to true.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I It is important to see that the unbiasedness result allow for
the βj to be any value, including zero.
I Suppose, then, that we specify the model
lwage = β0 + β1 educ + β2 exper + β3 motheduc + u
(63)
where MLR.1 through MLR.4 hold. Suppose that β3 = 0
(motheduc has no effect on lwage after educ and exper are
controlled for), but we do not know that. We estimate the full
model by OLS:
\ = β̂0 + β̂1 educ + β̂2 exper + β̂3 motheduc
lwage
ECON 371: Econometrics
The Multiple Regression Model
(64)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I We automatically know from the unbiasedness result that
E (β̂j ) = βj , j = 0, 1, 2
(65)
E (β̂3 ) = 0
(66)
I Hence, including an irrelavant variable, or overspecifying
the model, does not cause bias in any coefficients and it
follows from the general unbiasedness result. However, it is
not costless as the variances of the OLS estimators will suffer.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I Leaving a variable out when it should be included in multiple
regression is a serious problem. This is called excluding a
relevant variable or underspecifying the model.
I We can perform a misspecification analysis in this case.
The general case is more complicated. Consider the case
where the correct model has two explanatory variables:
y = β0 + β1 x1 + β2 x2 + u
satisfies MLR.1 through MLR.4.
ECON 371: Econometrics
The Multiple Regression Model
(67)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I If we regress y on x1 and x2 , we know the resulting estimators
will be unbiased. But suppose we leave out x2 and use simple
regression of y on x1 :
ỹ = β̃0 + β̃1 x1
(68)
I In most cases, we omit x2 because we cannot collect data on
it.
I We can easily derive the bias in β̃1 (conditional on the
outcomes of xi1 and xi2 ).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I We already have a relationship between β̃1 and the multiple
regression estimator, β̂1 :
β̃1 = β̂1 + β̂2 δ̃1
(69)
where β̂2 is the multiple regression estimator of β2 and δ̃1 is
the slope coefficient in the auxiliary regression
xi2 on xi1 , i = 1, ..., n
ECON 371: Econometrics
The Multiple Regression Model
(70)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I Now just use the fact that β̂1 and β̂2 are unbiased (or would
be if we could compute them):
E (β̃1 |x1 , x2 ) = E (β̂1 |x1 , x2 ) + E (β̂2 |x1 , x2 )δ̃1
(71)
= β1 + β2 δ̃1
(72)
I Therefore,
Bias(β̃1 ) = β2 δ̃1
(73)
I Recall that δ̃1 is a function of x1 , x2 and has the same sign as
Corr (x1 , x2 ).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
The simple regression estimator is unbiased in two cases.
1. β2 = 0. But this means that x2 does not appear in the
population model, so simple regression is the right thing to do.
2. Corr (x1 , x2 ) = 0 (in the sample). Then the simple and
multiple regression estimators are identical because δ̃1 = 0.
If β2 6= 0 and Corr (x1 , x2 ) 6= 0 then β̃1 is generally biased. We do
not know β2 and might only have a vague idea about the size of
δ̃1 . But we often can guess at the signs.
Bias in the Simple Regression Estimator of β1
Corr (x1 , x2 ) &gt; 0 Corr (x1 , x2 ) &lt; 0
β2 &gt; 0
Positive Bias
Negative Bias
β2 &lt; 0
Negative Bias
Positive Bias
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
EXAMPLE: Omitted Ability Bias.
lwage = β0 + β1 educ + β2 abil + u
(74)
where abil is “ability”. Essentially by definition, β2 &gt; 0. We also
think
Corr (educ, abil) &gt; 0
(75)
so that higher ability people get more education, on average. In
this scenario,
E (β̃1 ) &gt; β1
so there is an upward bias in simple regression.
ECON 371: Econometrics
The Multiple Regression Model
(76)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I Our failure to control for ability leads to (on average)
the effect of ability to education because ability and education
are positively correlated.
I Remember, for a particular sample, we can never know
whether β̃1 &gt; β1 . But we should be very hesitant to trust a
procedure that produces too large an estimate on average.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
EXAMPLE: Effects of a Tutoring Program on Student
Performance
GPA = β0 + β1 tutor + β2 abil + u
(77)
where tutor is hours spent in tutoring. Again, β2 &gt; 0. Suppose
that students with lower ability tend to use more tutoring:
Corr (tutor , abil) &lt; 0
(78)
E (β̃1 ) = β1 + β2 δ̃1 = β1 + (+)(−) &lt; β1
(79)
Then,
so that our failure to account for ability leads us to underestimate
the effect of tutoring. In fact, it could happen that β1 &gt; 0 but
E (β̃1 ) ≤ 0, so we tend to find no effect or even a negative effect.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I Determing bias is more difficult in situations with more than
two explanatory variables. The following simplication often
works. Suppose
lwage = β0 + β1 educ + β2 exper + β3 abil + u
(80)
and we omit ability. So we regress lwage on educ, exper. Call
these estimators β̃j , j = 0, 1, 2.
I Even if exper is uncorrelated with abil, β̃2 is generally biased,
just as is β̃1 . This is because educ and exper are usually
correlated.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Inclusion of Irrelevant Variables
Omitted Variable Bias
I If, as an approximation, we assume educ and exper are
uncorrelated, and exper and abil are uncorrelated, then the
bias analysis of β̃1 for the simpler case carries through, but it
is now β3 that matters. So, as a rough guide, β̃1 will have an
upward bias because β3 &gt; 0 and Corr (educ, abil) &gt; 0.
I In the general case, it should be remembered that correlation
of any xj with an omitted variable generally causes bias in all
of the OLS estimators, not just in β̃j
I See Appendix 3A in Wooldridge for a more detailed treatment.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
So far, we have assumed
I MLR.1: y = β0 + β1 x1 + β2 x2 + ... + βk xk + u
I MLR.2: random sampling
I MLR.3: no perfect collinearity
I MLR.4: E (u|x1 , x2 , ..., xk ) = 0
Under MLR.3 we can compute the OLS estimates in our
sample.
The other assumptions then ensure that OLS is unbiased
(when we think of repeated sampling).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I As in the simple regression case, to obtain Var (β̂j ) we add a
simplifying assumption: homoskedasticity (constant variance).
Assumption MLR.5 (Homoskedasticity)
The variance of the error, u, does not change with any of
x1 , x2 , ..., xk :
Var (u|x1 , x2 , ..., xk ) = Var (u) = σ 2
(81)
I This assumption can never be guaranteed. We make it for
now to get simple formulas, and to be able to discuss
efficiency of OLS.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Assumptions MLR.1 and MLR.4 imply
E (y |x1 , x2 , ..., xk ) = β0 + β1 x1 + ... + βk xk
(82)
Var (y |x1 , x2 , ..., xk ) = Var (u|x1 , x2 , ..., xk ) = σ 2
I Assumptions MLR.1 through MLR.5 are called the Gauss
Markov assumptions.
ECON 371: Econometrics
The Multiple Regression Model
(83)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I If we have a savings equation,
sav = β0 + β1 inc + β2 famsize + β3 pareduc + u
(84)
where famsize is size of the family and pareduc is total
parents’ education, MLR.5 means that the variance in sav
cannot depend in income, family size, or parents’s education.
I Later we will show how to relax MLR.5, and how to test
whether it is true.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I To set up the following theorem, we focus only on the slope
coefficients. (A different formula is needed for Var (β̂0 ))
I As before, we are computing the variance conditional on the
values of the explanatory variables in the sample.
I We need to define two quantities associated with each xj .
The first is the total variation in xj in the sample:
SSTj =
n
X
(xij − x̄j )2
i=1
(SSTj /n is the sample variance of xj .)
ECON 371: Econometrics
The Multiple Regression Model
(85)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I The second is a measure of correlation between xj and the
other explanatory variables, in the sample. This is the
R-squared from the regression
xij on xi1 , xi2 , ..., xi,j−1 , xi,j+1 , ..., xik
(86)
That is, we regress xj on all of the other explanatory variables
(y plays no role here). Call this R-squared Rj2 , j = 1, ..., k.
I Important: Rj2 = 1 is ruled out by Assumption MLR.3 because
Rj2 = 1 means xj is an exact linear function (in the sample) of
the other explanatory variables.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Any value 0 ≤ Rj2 &lt; 1 is allowed. As Rj2 gets closer to one, xj
is more linearly related to the other independent variables.
THEOREM (Sampling Variances of OLS Slope Estimators)
Under Assumptions MLR.1 to MLR.5,
Var (β̂j ) =
σ2
, j = 1, 2, ..., k.
SSTj (1 − Rj2 )
All five Gauss-Markov assumptions are needed to ensure this
formula is correct.
ECON 371: Econometrics
The Multiple Regression Model
(87)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
Suppose k = 3,
y = β0 + β1 x1 + β2 x2 + β3 x3 + u
(88)
E (u|x1 , x2 , x3 ) = 0
(89)
Var (u|x1 , x2 , x3 ) = γ0 + γ1 x1
(90)
where x1 ≥ 0 (as are γ0 and γ1 ). This violates MLR.5, and the
standard variance formula is generally incorrect for all OLS
estimators, not just Var (β̂1 ).
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
The variance
Var (β̂j ) =
Variances in Misspeficied Models
Estimating the Error Variance
σ2
SSTj (1 − Rj2 )
(91)
has three components. Two are familiar from simple regression.
The third, 1 − Rj2 , is new to multiple regression.
I As the error variance (in the population), σ 2 , decreases,
Var (β̂j ) decreases. One way to reduce the error variance is to
take more “stuff” out of the error. That is, add more
explanatory variables.
I As the total sample variation in xj , SSTj , increases, Var (β̂j )
decreases. As in the simple regression case, it is easier to
estimate how xj affects y if we see more variation in xj .
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
SSTj
j
I As we mentioned earlier, SST
n (or n−1 , the difference is
unimportant here) is the sample variance of
{xij : i = 1, 2, ..., n}. So we can use
SSTj ≈ nσj2
(92)
where σj2 &gt; 0 is the population variance of xj .
I We can increase SSTj by increasing the sample size. SSTj is
roughly a linear function of n. (Of the three components in
Var (β̂j ), this is the only one that depends systematically on
n.)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Var (β̂j ) =
Variances in Misspeficied Models
Estimating the Error Variance
σ2
SSTj (1 − Rj2 )
(93)
I As Rj2 → 1, Var (β̂j ) → ∞. Rj2 measures how linearly related
xj is to the other explanatory variables.
I We get the smallest variance for β̂j when Rj2 = 0:
Var (β̂j ) =
σ2
SSTj
looks just like the simple regression formula.
ECON 371: Econometrics
The Multiple Regression Model
(94)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I If xj is unrelated to all other independent variables, it is easier
to estimate its ceteris paribus effect on y .
I Rj2 = 0 is very rare. Even small values are not especially
common.
I In fact, Rj2 ≈ 1 is somewhat common, and this can cause
problems for getting a sufficient precise estimate of βj .
I Loosely, Rj2 “close” to one is called the “problem” of
multicollinearity. Unfortunately, we cannot define what we
mean by “close” that is relevant for all situations. We have
ruled out the case of perfect collinearity, Rj2 = 1.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Here is an important point: one often hears discussions of
multicollinearity as if high correlation among two or more of
the xj is a violation of an assumption we have made. But it
does not violate any of the Gauss-Markov assumptions.
I We know that if the zero conditional mean assumption is
violated, OLS is not unbiased. If MLR.1 through MLR.4 hold,
but homoskedasticity (constant variance) does not, then
Var (β̂j ) =
σ2
SSTj (1 − Rj2 )
(95)
is not the correct formula.
I But multicollinearity does not cause the OLS estimators to be
biased. We still have E (β̂j ) = βj .
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Further, the claim that the OLS variance formula is “biased”
is also wrong. The formula is correct under MLR.1 through
MLR.5.
I In fact, the formula is doing its job: it shows that if Rj2 is
“close” to one, Var (β̂j ) might be very large. If Rj2 is “close”
to one, xj does not have much variation separate from the
other explanatory variables. We are trying to estimate the
effect of xj on y , holding x1 , ..., xj−1 , xj+1 , ..., , xk fixed, but
the data might not be allowing us to do that very precisely.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Because multicollinearity violates none of our assumptions, it
is essentially impossible to state hard rules about when it is a
“problem”. Of course, this has not stopped some from trying.
I Other than just looking at the Rj2 , a common measure of
multicollinearity is called the variance inflation factor (VIF):
1
1 − Rj2
(96)
σ2
&middot; VIFj
SSTj
(97)
VIFj =
Var (β̂j ) =
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I The VIFj tells us how many times larger the variance is than if
we had the “ideal” case of no correlation of xij with
xi1 , ..., xi,j−1 , xi,j+1 , ..., , xik .
I This sometimes leads to silly rules-of-thumb. For example, one
should be “concerned” if VIFj &gt; 10 (equivalently, Rj2 &gt; .9).
I Is Rj2 &gt; .9 “large”? Yes, in the sense that it would be better
to have Rj2 smaller.
I But, if we want to control for, say, x2 , ..., xk to get a good
ceteris paribus effect of x1 on y , we often have no choice.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I A large VIFj can be offset by a large SSTj :
Var (β̂j ) =
σ2
&middot; VIFj
SSTj
(98)
With a large sample size, SSTj could be sufficiently large to
offset VIFj . The value of VIFj per se is irrelevant. Ultimately,
it is Var (β̂j ) that is important.
I Even so, at this point, we have no way of knowing whether
Var (β̂j ) is “too large” for the estimate β̂j to be useful. Only
when we discuss confidence intervals and hypothesis testing
will this be apparent.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Be wary of work that reports a set of multicollinearity
“diagnostics” and concludes nothing useful can be learned.
Sometimes any VIF about, say, 10 is used to make such a
claim. Other “diagnostics” are even more difficult to interpret.
Using them indiscriminately is often a mistake.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Consider an example:
y = β0 + β1 x1 + β2 x2 + β3 x3 + u
where β1 is the coefficient of interest. In fact, x2 and x3 act
as controls so that we hope to get a good ceteris paribus
estimate of x1 . Such controls are often highly correlated. But
the correlation between x2 and x3 has nothing to do with
Var (β̂1 ). It is only correlation of x1 with (x2 , x3 ) that matters.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I In an example to determine whether communities with larger
minority populations are discriminated against in lending, we
might have
percapproved = β0 + β1 percminority
+β2 avginc + β3 avghouseval + u
where β1 is the key coefficient. We might expect avginc and
avghouseval to be highly correlated across communities. But
we do not care about β2 and β3 .
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I As with bias calculations, we can study the variances of the
OLS estimators in misspecified models.
I Consider the same case with (at most) two explanatory
variables:
y = β0 + β1 x1 + β2 x2 + u
(99)
which we assume satisfies the Gauss-Markov assumptions.
I We run the “short” regression, y on x1 , and also the “long”
regression, y on x1 , x2 :
ỹ = β̃0 + β̃1 x1
(100)
ŷ = β̂0 + β̂1 x1 + β̂2 x2
(101)
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I We know from the previous analysis that
Var (β̂1 ) =
σ2
SST1 (1 − R12 )
(102)
conditional on the values xi1 and xi2 in the sample.
I What about the simple regression estimator? We can show
Var (β̃1 ) =
σ2
SST1
which is again conditional on {(xi1 , xi2 ) : i = 1, ..., n}.
ECON 371: Econometrics
The Multiple Regression Model
(103)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I Whenever xi1 and xi2 are correlated, R12 &gt; 0, and
Var (β̃1 ) =
σ2
σ2
&lt;
&lt; Var (β̂1 )
SST1
SST1 (1 − R12 )
(104)
I So, by omitting x2 , we can in fact get an estimator with a
smaller variance, even though it is biased. When we look at
bias and variance, we have a tradeoff between simple and
multiple regression.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
In the case R12 &gt; 0, we can draw two conclusions.
y = β0 + β1 x1 + β2 x2 + u
(105)
1. If β2 6= 0, β̃1 is biased, β̂1 is unbiased, but Var (β̃1 ) &lt; Var (β̂1 ).
2. If β2 = 0, β̃1 and β̂1 are both unbiased and
Var (β̃1 ) &lt; Var (β̂1 ).
Case 2 is clear cut. If β2 = 0, x2 has no (partial) effect on y .
When x2 is correlated with x1 , including it along with x1 in the
regression makes it more difficult to estimate the partial effect of
x1 . Simple regression is clearly preferred. This can be seen as the
cost of including irrelevant variables.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
Case 1 is more difficult, but there are reasons to prefer the unbiased
estimator, β̂1 . First, the bias in β̃1 does not systematically change
with the sample size. We should assume the bias is as large when
n = 1, 000 as when n = 10. By contrast, the variances Var (β̃1 )
and Var (β̂1 ) both shrink at the rate 1/n. With a large sample size,
the difference between Var (β̃1 ) and Var (β̂1 ) is less important,
especially considering the bias in β̃1 is not shrinking.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
Second reason for preferring β̂1 is more subtle. The formulas
Var (β̃1 ) =
σ2
SST1
(106)
Var (β̂1 ) =
σ2
SST1 (1 − R12 )
(107)
because they condition on the same explanatory variables, act as if
the error variance does not change when we add x2 . But if β2 6= 0,
the variance does shrink.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
In a more advanced course, we would be making a comparison
between
Var (β̃1 ) =
η2
SST1
(108)
Var (β̂1 ) =
σ2
SST1 (1 − R12 )
(109)
where η 2 &gt; σ 2 reflects the larger error variance in the simple
regression analysis.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
We still need to estimate σ 2 . With n observations and k + 1
parameters, we only have
df = n − (k + 1)
(110)
degrees of freedom. Recall we lose the k + 1 df due to k + 1
restrictions on the OLS residuals:
n
X
ûi
= 0
(111)
= 0, j = 1, 2, ..., k
(112)
i=1
n
X
xij ûi
i=1
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
THEOREM: (Unbiased Estimation of σ 2 )
Under the Gauss-Markov assumptions (MLR.1 through MLR.5)
σ̂ 2 = (n − k − 1)−1
n
X
i=1
ûi2 =
SSR
df
σ2.
(113)
is an unbiased estimator of
This means that, if we divide by n
rather than n − k − 1, the bias is
2 k +1
−σ
(114)
n
which means the estimated variance would be too small, on
average. The bias disappears as n increases.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
I The square root of σ̂ 2 , σ̂, is reported by all regression
packages. (standard error of the regression, or root mean
squared error).
I Note that SSR falls when a new explanatory variable is added,
but df falls, too. So σ̂ can increase or decrease when a new
variable is added in multiple regression.
I The standard error of each β̂j is computed (for the slopes)
as
σ̂
se(β̂j ) = q
SSTj (1 − Rj2 )
(115)
and it will be critical to report these along with the coefficient
estimates.
I We have discussed the three factors that affect se(β̂j ) already.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Variances in Misspeficied Models
Estimating the Error Variance
Using WAGE2.DTA:
\ = 5.198 + 0.057 educ + 0.0058IQ + 0.0195exper
lwage
(0.121)
(0.007)
(0.001)
(0.0032)
2
n = 935, R = 0.1622
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I How come we use OLS, rather than some other estimation
method?
I Consider simple regression:
y = β0 + β1 x + u
(116)
yi = β0 + β1 xi + ui .
(117)
and write, for each i,
I If we average across the n obervations we get
ȳ = β0 + β1 x̄ + ū
ECON 371: Econometrics
The Multiple Regression Model
(118)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I For any i with xi 6= x̄, subtract and rearrange:
β1 =
(yi − ȳ ) (ui − ū)
−
(xi − x̄) (xi − x̄)
(119)
I The last term has a zero expected value under random
sampling and E (u|x) = 0. If xi 6= x̄ for all i, we could use an
estimator
n
X
(yi − ȳ )
(120)
β̆1 = n−1
(xi − x̄)
i=1
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I β̆1 is not the same as the OLS estimator,
Pn
(xi − x̄)yi
β̂1 = Pi=1
n
2
i=1 (xi − x̄)
(121)
I How do we know OLS is better than this new estimator, β̆1 ?
I Generally, we do not. Under MLR.1 to MLR.4, both
estimators are unbiased.
I Comparing their variances is difficult in general. But, if we
add the homoskedasticity assumption, MLR.5, we can show
Var (β̂1 ) &lt; Var (β̆1 )
ECON 371: Econometrics
The Multiple Regression Model
(122)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I This means β̂1 has a sampling distribution that is less spread
out around β1 than β̆1 . When comparing unbiased estimators,
we prefer an estimator with smaller variance.
I We can make very general statements for the multiple
regression case, provided the 5 Gauss-Markov assumptions
hold.
I However, we must also limit the class of estimators that we
can compare with OLS.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
THEOREM (Gauss-Markov)
Under Assumptions MLR.1 through MLR.5, the OLS estimators
β̂0 , β̂1 , ..., β̂k are the best linear unbiased estimators (BLUEs).
Start from the end of “BLUE”.
I E (estimator): We must be able to compute an estimate
from the observable data, using a fixed rule.
I L (linear): The estimator is a linear function of yi . These
estimators have the general form
β̃j =
n
X
wij yi
(123)
i=1
where the wij are functions of (xi1 , ..., xik ). The OLS
estimators can be written in this way. It can be a nonlinear
function of the explanatory variables.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I U (unbiased): We must impose enough restrictions on the
wij – we omit those here – so that
E (β̃j ) = βj , j = 0, 1..., k
(conditional on {(xi1 , ..., xik ) : i = 1, ..., n}).
We know the OLS estimators are unbiased under MLR.1
through MLR.4. So are a lot of other linear estimators.
ECON 371: Econometrics
The Multiple Regression Model
(124)
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I B (best): This means smallest variance (which makes sense
once we impose unbiasedness). In other words, what can be
shown is that, under MLR.1 through MLR.5,
Var (β̂j ) ≤ Var (β̃j ) all j
(125)
(and usually the inequality is strict).
If we do not impose unbiasedness, then we can use silly rules –
such as β̃j = 1 always – to get estimators with zero variance.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
I How do we use the GM Theorem? If the Gauss-Markov
assumptions hold, and we insist on unbiased estimators that
are also linear functions of {yi : i = 1, 2, ..., n}, then we need
look no further than OLS: it delivers the smallest possible
variances.
I It might be possible (but even so, not practical) to find
unbiased estimators that are nonlinear functions of
{yi : i = 1, 2, ..., n} that have smaller variances than OLS.
The GM Theorem only allows linear estimators in the
comparison group.
I Appendix 3A contains a proof of the GM Theorem.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
If MLR.5 fails, that is, Var (u|x1 , ..., xk ) depends on one or more xj ,
the GM conclusions do not hold. There may be linear, unbiased
estimators of the βj with smaller variance.
ECON 371: Econometrics
The Multiple Regression Model
Motivation for Multiple Regression
Mechanics and Interpretation of OLS
The Expected Value of the OLS Estimators
The Variance of the OLS Estimators
Efficiency of OLS: The Gauss-Markov Theorem
Remember: Failure of MLR.5 does not cause bias in the β̂j , but it
does have two consequences:
1. The usual formulas for Var (β̂j ), and therefore se(β̂j ), are
wrong.
2. The β̂j are no longer BLUE.
The first of these is more serious, as it will directly affect statistical
inference (next). The second consequence means we may want to
search for estimators other than OLS. This is not so easy. And
with a large sample, it may not be very important.
ECON 371: Econometrics
The Multiple Regression Model
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