Econometrics-I-7

# Estimated variance 4.54799e-6 4(43.5272)2(5.87973e-10

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Unformatted text preview: Estimated Variance 4.54799e-6 + 4(43.5272)2(5.87973e-10) + 4(43.5272)(-5.1285e-8) = 7.4755086e-08. Estimated standard error = .00027341. &#152;&#152;&#152;&#152; &#152;™ 33/35 Part 7: Estimating the Variance of b Specification and Functional Form: Interaction Effect 1 2 3 4 1 2 3 4 2 4 2 4 2 2 Population Estimators ˆ [ | , ] ˆ z ˆ Estimator of the variance of ˆ . [ ] [ ] x x x x y x z xz y b b x b z b xz E y x z b b z x Est Var Var b z Va = β + β + β + β + ε = + + + ∂ δ = = β + β δ = + ∂ δ δ = + 4 2 4 [ ] 2 [ , ] r b zCov b b + &#152;&#152;&#152;&#152; &#152;™ 34/35 Part 7: Estimating the Variance of b Interaction Effect---------------------------------------------------------------------- Ordinary least squares regression ............ LHS=LOGY Mean = -1.15746 Standard deviation = .49149 Number of observs. = 27322 Model size Parameters = 4 Degrees of freedom = 27318 Residuals Sum of squares = 6540.45988 Standard error of e = .48931 Fit R-squared = .00896 Adjusted R-squared = .00885 Model test F[ 3, 27318] (prob) = 82.4(.0000)--------+------------------------------------------------------------- Variable| Coefficient Standard Error b/St.Er. P[|Z|>z] Mean of X--------+------------------------------------------------------------- Constant| -1.22592*** .01605 -76.376 .0000 AGE| .00227*** .00036 6.240 .0000 43.5272 FEMALE| .21239*** .02363 8.987 .0000 .47881 AGE_FEM| -.00620*** .00052 -11.819 .0000 21.2960--------+------------------------------------------------------------- Do women earn more than men (in this sample?) The +.21239 coefficient on FEMALE would suggest so. But, the female “difference” is +.21239 - .00620*Age. At average Age, the effect is .21239 - .00620(43.5272) = -.05748. &#152;&#152;&#152;&#152; &#152; 35/35 Part 7: Estimating the Variance of b Part 7: Estimating the Variance of b...
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Estimated Variance 4.54799e-6 4(43.5272)2(5.87973e-10...

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