Email: support@essaywriterpros.com
Call Us: US - +1 845 478 5244 | UK - +44 20 7193 7850 | AUS - +61 2 8005 4826

conditional Expectation Function .

tic.
4.11 Estimation of Error Variance
The error variance 
2 = E

e
2
i

can be a parameter of interest even in a heteroskedastic regression
or a projection model. 
2 measures the variation in the ìunexplainedî part of the regression. Its
method of moments estimator (MME) is the sample average of the squared residuals:
b
2 =
1
n
Xn
i=1
eb
2
i
:
In the linear regression model we can calculate the mean of b
2
: From (3.29) and the properties
of the trace operator, observe that
b
2 =
1
n
e
0M e =
1
n
tr
e

0M e

1
n
tr M ee0

:
Then
E

b
2
j X



1
n
tr
E
M ee0
j X



1
n
tr ME

ee0
j X



1
n
tr (MD): (4.25)
Adding the assumption of conditional homoskedasticity E

e
2
i
j xi

= 
2
; so that D = In
2
; then
(4.25) simpliÖes to
E

b
2
j X



1
n
tr M
2



CHAPTER 4. LEAST SQUARES REGRESSION 115
the Önal equality by (3.23). This calculation shows that b
2
is biased towards zero. The order of
the bias depends on k=n, the ratio of the number of estimated coe¢ cients to the sample size.
Another way to see this is to use (4.22). Note that
E

b
2
j X



1
n
Xn
i=1
E

eb
2
i
j X



1
n
Xn
i=1
(1 hii) 

2


n k
n


2
the last equality using Theorem 3.6.
Since the bias takes a scale form, a classic method to obtain an unbiased estimator is by rescaling
the estimator. DeÖne
s
2 =
1
n k
Xn
i=1
eb
2
i
: (4.26)
By the above calculation,
E

s
2
j X

= 
2
and
E

s
2

= 
2
:
Hence the estimator s
2
is unbiased for 
2
: Consequently, s
2
is known as the ìbias-corrected estimatorî for 
2 and in empirical practice s
2
is the most widely used estimator for 
2
:
Interestingly, this is not the only method to construct an unbiased estimator for 
2
. An estimator constructed with the standardized residuals ei from (4.23) is

2 =
1
n
Xn
i=1
e
2
i =
1
n
Xn
i=1
(1 hii)
1
eb
2
i
:
You can show (see Exercise 4.9) that
E


2
j X

= 
2
(4.27)
and thus 
2
is unbiased for 
2
(in the homoskedastic linear regression model).
When k=n is small (typically, this occurs when n is large), the estimators b
2
; s2 and 
2 are
likely to be similar to one another. However, if k=n is large then s
2 and 
2 are generally preferred
to b
2
: Consequently it is best to use one of the bias-corrected variance estimators in app