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15.1 Grunfeld’s Investment Data
15.2 Sets of Regression Equations
15.3 Seemingly Unrelated Regressions
15.4 The Fixed Effects Model
15.4 The Random Effects Model
The different types of panel data sets can be described as:
“long and narrow,” with “long” describing the time dimension and
“narrow” implying a relatively small number of cross sectional units;
“short and wide,” indicating that there are many individuals observed
over a relatively short period of time;
“long and wide,” indicating that both N and T are relatively large.
The data consist of T = 20 years of data (1935-1954) for N = 10 large firms.
Let yit = INVit and x2it = Vit and x3it = Kit
,it it itINV f V K
1 2 2 3 3it it it it it it ity x x e
, 1 2 , 3 , ,
, 1 2 , 3 , ,
1, ,20
1, ,20
GE t GE t GE t GE t
WE t WE t WE t WE t
INV V K e t
INV V K e t
1 2 2 3 3 1, 2; 1, ,20it it it ity x x e i t
, 1, 2, , 3, , ,
, 1, 2, , 3, , ,
1, ,20
1, ,20
GE t GE GE GE t GE GE t GE t
WE t WE WE WE t WE WE t WE t
INV V K e t
INV V K e t
1 2 2 3 3 1, 2; 1, ,20it i i it i it ity x x e i t
Assumption (15.5) says that the errors in both investment functions (i) have zero mean, (ii) are homoskedastic with constant variance, and (iii) are not correlated over time; autocorrelation does not exist. The two equations do have different error variances
2, , , ,
2, , , ,
0 var cov , 0
0 var cov , 0
GE t GE t GE GE t GE s
WE t WE t WE WE t WE s
E e e e e
E e e e e
2 2 and .GE WE
Let Di be a dummy variable equal to 1 for the Westinghouse
observations and 0 for the General Electric observations.
1, 1 2, 2 3, 3it GE i GE it i it GE it i it itINV D V D V K D K e
This assumption says that the error terms in the two equations, at the same point in time, are correlated. This kind of correlation is called a contemporaneous correlation.
, , ,cov ,GE t WE t GE WEe e
Econometric software includes commands for SUR (or SURE) that
carry out the following steps:
(i) Estimate the equations separately using least squares;
(ii)Use the least squares residuals from step (i) to estimate
;
(iii)Use the estimates from step (ii) to estimate the two equations jointly
within a generalized least squares framework.
2 2,, and GE WE GE WE
There are two situations where separate least squares estimation is
just as good as the SUR technique :
(i) when the equation errors are not contemporaneously correlated;
(ii)when the same explanatory variables appear in each equation.
If the explanatory variables in each equation are different, then a test
to see if the correlation between the errors is significantly different
from zero is of interest.
In this case
22,2
, 2 2
ˆ 207.58710.53139
ˆ ˆ 777.4463 104.3079GE WE
GE WEGE WE
r
20 20
, , , , ,1 1
1 1ˆ ˆ ˆ ˆ ˆ
3GE WE GE t WE t GE t WE tt tGE WE
e e e eTT K T K
3.GE WEK K
Testing for correlated errors for two equations:
LM = 10.628 > 3.84
Hence we reject the null hypothesis of no correlation between the
errors and conclude that there are potential efficiency gains from
estimating the two investment equations jointly using SUR.
0 ,: 0GE WEH
2 2, (1) 0 under .GE WELM Tr H
Testing for correlated errors for three equations:
0 12 13 23: 0H
2 2 2 212 13 23 (3)LM T r r r
Testing for correlated errors for M equations:
Under the null hypothesis that there are no contemporaneous
correlations, this LM statistic has a χ2-distribution with M(M–1)/2
degrees of freedom, in large samples.
12
2 1
M i
iji j
LM T r
Most econometric software will perform an F-test and/or a Wald χ2–test; in the context of SUR equations both tests are large sample approximate tests.
The F-statistic has J numerator degrees of freedom and (MTK) denominator degrees of freedom, where J is the number of hypotheses, M is the number of equations, and K is the total number of coefficients in the whole system, and T is the number of time series observations per equation. The χ2-statistic has J degrees of freedom.
0 1, 1, 2, 2, 3, 3,: , ,GE WE GE WE GE WEH
We cannot consistently estimate the 3×N×T parameters in (15.9) with only NT total observations.
1 2 2 3 3it it it it it it ity x x e
1 1 2 2 3 3, ,it i it it
All behavioral differences between individual firms and over time are
captured by the intercept. Individual intercepts are included to
“control” for these firm specific differences.
1 2 2 3 3it i it it ity x x e
This specification is sometimes called the least squares dummy
variable model, or the fixed effects model.
1 2 3
1 1 1 2 1 3, , , etc.
0 otherwise 0 otherwise 0 otherwisei i i
i i iD D D
11 1 12 2 1,10 10 2 2 3 3it i i i it it itINV D D D V K e
These N–1= 9 joint null hypotheses are tested using the usual F-test
statistic. In the restricted model all the intercept parameters are equal.
If we call their common value β1, then the restricted model is:
0 11 12 1
1 1
:
: the are not all equal
N
i
H
H
1 2 3it it it itINV V K e
We reject the null hypothesis that the intercept parameters for all
firms are equal. We conclude that there are differences in firm
intercepts, and that the data should not be pooled into a single model
with a common intercept parameter.
1749128 522855 948.99
522855 200 12
R U
U
SSE SSE JF
SSE NT K
1 2 2 3 3 1, ,it i it it ity x x e t T
1 2 2 3 31
1 T
it i it it itt
y x x eT
1 2 2 3 31 1 1 1
1 2 2 3 3
1 1 1 1T T T T
i it i it it itt t t t
i i i i
y y x x eT T T T
x x e
1 2 2 3 3
1 2 2 3 3
2 2 2 3 3 3
( )
( ) ( ) ( )
it i it it it
i i i i i
it i it i it i it i
y x x e
y x x e
y y x x x x e e
2 3it it it ity x x e
.1098 .3106
(se*) (.0116) (.0169)
itit itINV V K
2*ˆ 2e SSE NT
2 2 198 188 1.02625NT NT N
1 2 2 3 3i i i iy b b x b x
1 2 2 3 3 1, ,i i i ib y b x b x i N
1 1i iu
20, cov , 0, vari i j i uE u u u u
1 2 2 3 3
1 2 2 3 3
it i it it it
i it it it
y x x e
u x x e
Because the random effects regression error in (15.24) has two
components, one for the individual and one for the regression, the
random effects model is often called an error components model.
1 2 2 3 3
1 2 2 3 3
it it it it i
it it it
y x x e u
x x v
it i itv u e
0 0 0it i it i itE v E u e E u E e
2
2 2
var var
var var 2cov ,
v it i it
i it i it
u e
v u e
u e u e
There are several correlations that can be considered.
The correlation between two individuals, i and j, at the same
point in time, t. The covariance for this case is given by
cov , ( )
0 0 0 0 0
it jt it jt i it j jt
i j i jt it j it jt
v v E v v E u e u e
E u u E u e E e u E e e
The correlation between errors on the same individual (i) at
different points in time, t and s. The covariance for this case is
given by
2
2 2
cov , ( )
0 0 0
it is it is i it i is
i i is it i it is
u u
v v E v v E u e u e
E u E u e E e u E e e
The correlation between errors for different individuals in
different time periods. The covariance for this case is
cov , ( )
0 0 0 0 0
it js it js i it j js
i j i js it j it js
v v E v v E u e u e
E u u E u e E e u E e e
2
2 2
cov( , )corr( , )
var( ) var( )it is u
it isu eit is
v vv v
v v
1 2 2 3 3it it it ity x x e
1 2 2 3 3it it it ite y b b x b x
2
1 1
2
1 1
ˆ1
2 1 ˆ
N T
iti t
N T
iti t
eNT
LMT e
* * * * *1 1 2 2 3 3it it it it ity x x x v
* * * *1 2 2 2 3 3 3, 1 , ,it it i it it it i it it iy y y x x x x x x x
2 21 e
u eT
2 2
ˆ .1951ˆ 1 1 .7437
5 .1083 .0381ˆ ˆe
u eT
If the random error is correlated with any of the right-
hand side explanatory variables in a random effects model then the
least squares and GLS estimators of the parameters are biased and
inconsistent.
it i itv u e
1 2 2 3 31 1 1 1 1
1 2 2 3 3
1 1 1 1 1T T T T T
i it it it i itt t t t t
i i i i
y y x x u eT T T T T
x x u e
1 2 2 3 3 ( )it it it i ity x x u e
1 2 2 3 3
1 2 2 3 3
2 2 2 3 3 3
( )
( ) ( ) ( )
it it it i it
i i i i i
it i it i it i it i
y x x u e
y x x u e
y y x x x x e e
We expect to find
because Hausman proved that
, , , ,
1 2 1 22 2
, ,, ,se sevar var
FE k RE k FE k RE k
FE k RE kFE k RE k
b b b bt
b bb b
, ,var var 0.FE k RE kb b
, , , , , ,
, ,
var var var 2cov ,
var var
FE k RE k FE k RE k FE k RE k
FE k RE k
b b b b b b
b b
, , ,cov , var .FE k RE k RE kb b b
The test statistic to the coefficient of SOUTH is:
Using the standard 5% large sample critical value of 1.96, we reject the hypothesis that the estimators yield identical results. Our conclusion is that the random effects estimator is inconsistent, and we should use the fixed effects estimator, or we should attempt to improve the model specification.
, ,
1 2 1 22 2 2 2
, ,
.0163 (.0818) 2.3137
.0361 .0224se se
FE k RE k
FE k RE k
b bt
b b
Slide 15-50Principles of Econometrics, 3rd Edition
Slide 15-51Principles of Econometrics, 3rd Edition
Principles of Econometrics, 3rd Edition Slide 15-52
(15A.1)
(15A.2)
(15A.3)
1 2 2 3 3 ( )it it it i ity x x u e
2 2 2 3 3 3( ) ( ) ( )it i it i it i it iy y x x x x e e
2ˆ DVe
slopes
SSE
NT N K
Principles of Econometrics, 3rd Edition Slide 15-53
(15A.4)
(15A.5)
1 2 2 3 3 1, ,i i i i iy x x u e i N
1
22 2
2 21
22
var var var var var
1var
T
i i i i i itt
Te
u it ut
eu
u e u e u e T
Te
T T
T
Principles of Econometrics, 3rd Edition Slide 15-54
(15A.6)
(15A.7)
22 e BEu
BE
SSE
T N K
2 2
2 2 ˆˆ e e BE DV
u uBE slopes
SSE SSE
T T N K T NT N K