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Econometric take home APPS_Part_2

# Econometric take home APPS_Part_2 - Each of these is the...

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Each of these is the slope coefficient in the simple of y on the respective variable. ================================================= 1 hered,sibs\$ ================================= ---+----------+ >t]| Mean of X| ---+----------+ >t]| Mean of X| Application ?====================== Chapter 3 Application ? ?======================================================================= Read \$ (Data appear in the text.) t ; X1 = one,educ,exp,ability\$ Namelis Namelist ; X2 = mothered,fat ?====================================== ? a. ?======================================================================= ss ; Lhs = wage ; Rhs = x1\$ Regre +----------------------------------------------------+ sion | | Ordinary least squares regres | LHS=WAGE Mean = 2.059333 | | Standard deviation = .2583869 | | WTS=none Number of observs. = 15 | | Model size Parameters = 4 | | Degrees of freedom = 11 | | Residuals Sum of squares = .7633163 | | Standard error of e = .2634244 | | Fit R-squared = .1833511 | | Adjusted R-squared = -.3937136E-01 | | Model test F[ 3, 11] (prob) = .82 (.5080) | +----------------------------------------------------+ +--------+--------------+----------------+--------+----- |Variable| Coefficient | Standard Error |t-ratio |P[|T| +--------+--------------+----------------+--------+--------+----------+ Constant| 1.66364000 .61855318 2.690 .0210 EDUC | .01453897 .04902149 .297 .7723 12.8666667 2.80000000 EXP | .07103002 .04803415 1.479 .1673 ABILITY | .02661537 .09911731 .269 .7933 .36600000 ?======================================================================= ? b. ?======================================================================= ss ; Lhs = wage ; Rhs = x1,x2\$ Regre +----------------------------------------------------+ n | | Ordinary least squares regressio | LHS=WAGE Mean = 2.059333 | | Standard deviation = .2583869 | | WTS=none Number of observs. = 15 | | Model size Parameters = 7 | | Degrees of freedom = 8 | | Residuals Sum of squares = .4522662 | | Standard error of e = .2377673 | | Fit R-squared = .5161341 | | Adjusted R-squared = .1532347 | | Model test F[ 6, 8] (prob) = 1.42 (.3140) | +----------------------------------------------------+ +--------+--------------+----------------+--------+----- |Variable| Coefficient | Standard Error |t-ratio |P[|T| +--------+--------------+----------------+--------+--------+----------+ Constant| .04899633 .94880761 .052 .9601 EDUC | .02582213 .04468592 .578 .5793 12.8666667 2.80000000 EXP | .10339125 .04734541 2.184 .0605 ABILITY | .03074355 .12120133 .254 .8062 .36600000 MOTHERED| .10163069 .07017502 1.448 .1856 12.0666667 FATHERED| .00164437 .04464910 .037 .9715 12.6666667 SIBS | .05916922 .06901801 .857 .4162 2.20000000 ?======================================================================= ? c. ?======================================================================= 7

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Regress ; Lhs = mothered ; Rhs = x1 ; Res = meds \$ Regress ; Lhs = fathered ; Rhs = x1 ; Res = feds \$ Regress ; Lhs = sibs ; Rhs = x1 ; Res = sibss \$ Namelist ; X2S = meds,feds,sibss \$ Matrix ; list ; Mean(X2S) \$ olumns. Matrix Result has 3 rows and 1 c 1 +-------------- 1| -.1184238D-14 2| .1657933D-14 3| -.5921189D-16 The means are (essentially) zero. The sums must be zero, as these new variables ) \$ 0*X*b12 \$ 12 \$ ym0y * e'e \$ od of computation.
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