Introduction to econometrics II eco 356 faculty of social sciences course guide course Developers: Dr. Adesina-Uthman



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Introduction to Econometrics ECO 356 Course Guide and Course Material
INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
75 the true unknown parameters. Our earlier stated definition of goodness of fit is the minimization of RSS, which is the sum of squares of the residuals

…[2.50] Where e
i
is again, the residual in observation i, the difference between the actual value
Y
i
in that observation and the value predicted by the regression equation
̂
…[2.51]
̂
…[2.52] It could be observed that the X variables now have two subscripts. The first identifies the X variable and the second identifies the observation. Applying [2.52] into [2.50];
…[2.53] From first-order conditions fora minimum
[2.52] will give the following equations

(
)
…[2.54]

(
)
…[2.55]

(
)
…[2.56] Resulting in three equations from the three unknowns, b
1
, b
2
, and b
3
The first can easily be rearranged to express b
1
regardingb
2
, b
3
, and the data on Y, X
2
, and X
3
:
̅
̅̅̅
̅̅̅
…[2.57]


INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
76 From [3.57] and working through [3.55] to [3.56], the following expression for b
2
is obtained
(
) (
) (
) (
)
(
) (
) , (
)-
,∑(
)(
)- ,∑(
)(
)-
,∑(
)∑(
) ∑,(
)-
…[2.58] Similarly, theexpressionofb
3
can be obtained by switching X
2
and X
3
in [2.58]. Clearly, the principles behind the derivation of the regression coefficients have been shown to be the same for multiple regression as that of the simple regression. But, it should also be observed that the expressions are however different and so should not try to use expressions derived for simple regression in a multiple regression situations. A generalized framework for the multiple regression model is
1 2
2
i
i
k
ki
i
Y
X
X
 


 


…[2.59] We may write [2.59] for three variables as,
1 2
2 3
3
i
i
i
i
Y
X
X
 


 


…[2.60] whereY is the dependent variable, and
k
X
X
(kth term) the regressors, the stochastic disturbance term and i the ith (tth, if in time series) observation. Also
1 and
k
 are the partial regression coefficients but is the intercept term which gives the mean effect on Y of all the variables excluded from the model. That is, in the case of [2.50], when and
k
X
X
are set equal to zero. Zero mean value of
i

in [2.60] is
2 3
(
|
,
)
0
i
i
i
E
X
X


…[2.61]

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