qualitative-2&3 - QUALITATIVE INFORMATION-2...

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QUALITATIVE INFORMATION QUALITATIVE INFORMATION - - 2 2 Linear Probability Models Linear Probability Models
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y = { 0,1 } e.g . y = 1 if woman is in labour force y = 2 if woman is not in labour force p ( x ) = measures the change in the probability of a woman being in the labour force when x(j) increases by a unit, ceteris paribus . i.e. marginal effect of x(j) on Prob ( y | x ) 01 12 2 ... kk yx xx u β ββ =+ + + + + 10 1 1 2 2 [| , . . . ] . . . k E yx x x x x + + + 11 1 [ | ,... ] 0 ( 0| ) 1 ( 1| ) k E P r o by x x P r o =⋅ = + = 0 1 1 2 2 ( ) ( ) ... k k px x P r ob y x x x x x = == + + + + 1 (| , . . . ) k j j Prob y x x x =
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Example: Pr ( in labour force ) = 0.58 – 0.0034 ( husband’s earning ) + 0.0038 ( educ ) (0.154) (0.0014) (0.007) + 0.0039 ( exper ) – 0.0006( exper-square ) – 0.016( age ) (0.006) (0.00018) (0.002) – 0.262(# kids < 6 years old ) + 0.013(# kids > 6 years old ) (0.034) (0.0132) n = 753; R-square = 0.264 Weakness (i) p ( x ) < 0, p ( x ) > 0, depending on the value of x . (ii) a probability cannot be linear to the independent variable for all possible values (i.e. LPM imposes constant marginal/partial effect). (iii) var [ y | x ] = p ( x )[1- p ( x ) ] ~ x violates MLR/5.
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This note was uploaded on 08/27/2011 for the course ECON 7043 taught by Professor Projim during the Three '11 term at University of Adelaide.

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qualitative-2&amp;amp;3 - QUALITATIVE INFORMATION-2...

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