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


Least Squares Regression with One Explanatory Variable



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Introduction to Econometrics ECO 356 Course Guide and Course Material
2.1.3.3.1 Least Squares Regression with One Explanatory Variable
We shall now consider the general case where there are n observations on two variables X and Y and supposing Y to depend on X; we will fit the equation
̂
...[2.12] The fitted value of the dependent variable in observation i. will be (b
1
+ b
2
X
i
) and the residual will be (Y
i
b
1
b
2
X
i
). We wish to choose b
1
and b
2 so as to minimize the residual sum of the squares RSS given by


INTRODUCTION TO ECONOMETRICS II

ECO 306

NOUN
49

...[2.13] We will find that RSS is minimised when
( )
( )
…[2.14] And
̅
̅
…[2.15] The derivation of the expressions for b
1
and b
2
will follow the same procedure as the derivation in the preceding example, and you can compare the general version with the examples at each step. We will begin by expressing the square of the residual in observation iregardingb
1
, b
2
and the data on X and Y:
(
̂
)
(
)
…[2.16] Summing overall the nobservations, we can write RSS as
(
)
(
)





…[2.17] Note that RSS is effectively a quadratic expression in b
1
and b
2
, with numerical coefficients determined by the data on X and Y in the sample. We can influence the size of RSS
only through our choice of b
1
and b
2
. The data on X and Y, which determine the locations of the observations in the scatter diagram and are fixed once we have taken the sample. This equation [2.17] is the generalized version of the equations. The first order conditions fora minimum,
…[2.18] Yield the following equations



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