Wooldridge Solution chapter 3
March 27, 2017 | Author: Adami Fajri | Category: N/A
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C3.1 (i) (ii)
Positive Yes. Positive: High income, can buy more goods Negative: base on fatheduc, most family have high education and know the danger of smoking, so high income family with high education tend not to smoke CIGS 1.000000 -0.173045
CIGS FAMINC
(iii)
FAMINC -0.173045 1.000000
cigs Dependent Variable: BWGHT Method: Least Squares Date: 12/20/14 Time: 16:35 Sample: 1 1388 Included observations: 1388 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C CIGS
119.7719 -0.513772
0.572341 0.090491
209.2668 -5.677609
0.0000 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.022729 0.022024 20.12858 561551.3 -6135.457 32.23524 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
118.6996 20.35396 8.843598 8.851142 8.846420 1.924390
faminc Dependent Variable: BWGHT Method: Least Squares Date: 12/20/14 Time: 16:37 Sample: 1 1388 Included observations: 1388 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C CIGS FAMINC
116.9741 -0.463408 0.092765
1.048984 0.091577 0.029188
111.5118 -5.060315 3.178195
0.0000 0.0000 0.0015
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
C3.2
0.029805 0.028404 20.06282 557485.5 -6130.414 21.27392 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
118.6996 20.35396 8.837772 8.849089 8.842005 1.921690
(i)
result: price = -19,315 + 0,128 sqrft + 15,198 bdrms Dependent Variable: PRICE Method: Least Squares Date: 12/20/14 Time: 16:50 Sample: 1 88 Included observations: 88 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C SQRFT BDRMS
-19.31500 0.128436 15.19819
31.04662 0.013824 9.483517
-0.622129 9.290506 1.602590
0.5355 0.0000 0.1127
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) (ii) (iii) (iv) (v) (vi)
C3.3
0.631918 0.623258 63.04484 337845.4 -487.9989 72.96353 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
$15.198,19
$ 33,17923 R2= 63% 354.600 Estimate price = 354.600 Since actual =300.000 Then =300.000-354.600=-54.600 (underpaid)
293.5460 102.7134 11.15907 11.24352 11.19309 1.858074
(i)
Log (salary) = B0 + B1 log (sales) + B3 log (mktval) Log (salary) = 4,621 + 0,162 log (sales) + 0,107 log (mktval) Dependent Variable: LSALARY Method: Least Squares Date: 12/20/14 Time: 19:38 Sample: 1 177 Included observations: 177 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LSALES LMKTVAL
4.620918 0.162128 0.106708
0.254408 0.039670 0.050124
18.16339 4.086899 2.128880
0.0000 0.0001 0.0347
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) (ii)
0.299114 0.291057 0.510294 45.30966 -130.5594 37.12852 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
6.582848 0.606059 1.509146 1.562979 1.530979 2.092115
Log (salary) = B0 + B1 log (sales) + B3 log (mktval) + B3 profits Log (salary) = 4,687 + 0,161 log (sales) + 0,097 log (mktval) + 3,57*10-5 profits Dependent Variable: LSALARY Method: Least Squares Date: 12/20/14 Time: 19:42 Sample: 1 177 Included observations: 177 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LSALES LMKTVAL PROFITS
4.686924 0.161368 0.097529 3.57E-05
0.379729 0.039910 0.063689 0.000152
12.34280 4.043299 1.531333 0.234668
0.0000 0.0001 0.1275 0.8147
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.299337 0.287186 0.511686 45.29524 -130.5312 24.63628 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
6.582848 0.606059 1.520127 1.591904 1.549237 2.096546
The R2 is almost the same, including variable profits only gives small influence to the model. 70% of variation in log salary is unexplained. (iii)
Log (salary) = B0 + B1 log (sales) + B3 log (mktval) + B3 profits + B4 ceoten Log (salary) = 4,558 + 0,162 log (sales) + 0,102 log (mktval) + 2,91*10-5 profits + 0,012 ceoten
Dependent Variable: LSALARY Method: Least Squares Date: 12/20/14 Time: 19:49 Sample: 1 177 Included observations: 177
C3.4 (i)
Descriptive stat ATNDRTE 81.70956 87.50000 100.0000 6.250000 17.04699 -1.578799 5.693665
PRIGPA 2.586775 2.560000 3.930000 0.857000 0.544714 0.161246 2.760582
ACT 22.51029 22.00000 32.00000 13.00000 3.490768 0.075404 2.645701
Jarque-Bera Probability
488.0774 0.000000
4.570791 0.101734
4.200994 0.122396
Sum Sum Sq. Dev.
55562.50 197317.3
1759.007 201.4684
15307.00 8273.928
680
680
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
(ii)
Observations 680 Estimate the model Atndrte = 75,70 + 17,261 prigpa – 1,717 act
The intercept of 75.70 is the predicted percent of classes attended for a student with 0 cumulative GPA prior to the current term and an ACT score of 0. I would not call this particular meaning “useful.” The intercept is useful, but its interpretation is not. Dependent Variable: ATNDRTE Method: Least Squares Date: 12/20/14 Time: 20:09 Sample: 1 680 Included observations: 680 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C PRIGPA ACT
75.70041 17.26059 -1.716553
3.884108 1.083103 0.169012
19.48978 15.93624 -10.15640
0.0000 0.0000 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic) (iii)
(iv)
(v)
C3.5
0.290581 0.288486 14.37936 139980.6 -2776.115 138.6513 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
81.70956 17.04699 8.173867 8.193817 8.181589 2.010991
Additional point for GPA will increase the class attendance. However, additional score for ACT test will decrease the class attendance. Unexpected result. Perhaps gaining high score means that student thinks they do not have the necessity to attend the class
104,36 would seem to be a very good student. But no student attends more than 100% of classes! (observation number 569). The model provides residual The difference in predicted attendance between Student A and Student B is 93.09 - 67.23= 25.86%
log(wage) =0.284+ 0.092 educ + 0.0041 exper + 0.022 tenure. Dependent Variable: LWAGE Method: Least Squares Date: 12/20/14 Time: 20:35 Sample: 1 526 Included observations: 526 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C EDUC EXPER TENURE
0.284360 0.092029 0.004121 0.022067
0.104190 0.007330 0.001723 0.003094
2.729230 12.55525 2.391437 7.133071
0.0066 0.0000 0.0171 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.316013 0.312082 0.440862 101.4556 -313.5478 80.39092 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
1.623268 0.531538 1.207406 1.239842 1.220106 1.768805
Partialling out on educ coefficient What we are doing is trying to find the effect of educ on log(wage), controlling for exper and tenure This effect is equal to the effect on log(wage) of the portion of educ that is NOT explained by exper and tenure. First we need to construct a variable that is equal to the portion of educ that is not explained by exper and tenure. The easiest way to do that is to take the residual from the regression: Educ = g0 + g1 exper + g2 tenure + u Educ = 13,574 – 0,074 exper + 0,048 tenure Dependent Variable: EDUC Method: Least Squares Date: 12/20/14 Time: 20:41 Sample: 1 526 Included observations: 526 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C EXPER TENURE
13.57496 -0.073785 0.047680
0.184324 0.009761 0.018337
73.64710 -7.559282 2.600162
0.0000 0.0000 0.0096
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.101342 0.097906 2.629980 3617.483 -1253.487 29.48955 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
To find the residuals in this regression I subtract educ from educ:
Dependent Variable: LWAGE
12.56274 2.769022 4.777517 4.801843 4.787042 1.869826
C3.6 (i) EDUC
3.533829
0.192210
18.38530
0.0000
EDUC
0.059839
0.005963
10.03492
0.0000
EDUC IQ
0.039120 0.005863
0.006838 0.000998
5.720784 5.875413
0.0000 0.0000
(ii)
(iii)
(iv)
C3.7
(i) Dependent Variable: MATH10 Method: Least Squares Date: 12/20/14 Time: 21:16 Sample: 1 408 Included observations: 408 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LEXPEND LNCHPRG
-20.36076 6.229691 -0.304585
25.07287 2.972634 0.035357
-0.812063 2.095680 -8.614468
0.4172 0.0367 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.179927 0.175877 9.526228 36753.36 -1497.073 44.42926 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
24.10686 10.49361 7.353301 7.382795 7.364972 1.902822
math10 = -20,36 + 6,23 lexpend – 0,305 lnchprg The sign of the coefficients are as expected: the percentage of students passing a math exam is increasing in expenditure per student and decreasing in the percentage of students who are in a school lunch program (presumably a subsidized lunch program) (ii) (iii)
No. for lexpend cannot set to 0 because log 0 = undefined. At least $1 for lexpend. For lnchprg we can set it to 0 Math10 with lexpend
Dependent Variable: MATH10 Method: Least Squares Date: 12/20/14 Time: 21:27 Sample: 1 408 Included observations: 408 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LEXPEND
-69.34108 11.16439
26.53013 3.169011
-2.613673 3.522990
0.0093 0.0005
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.029663 0.027273 10.34953 43487.76 -1531.396 12.41146 0.000475
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
24.10686 10.49361 7.516649 7.536312 7.524429 1.614623
The magnitude of the slope coefficient has gotten larger. It was previously 6.23 and is now 11.16. This speaks to a negative correlation between log(expend) and lnchprg. (iv)
correlation between lexpend log(expend) and lnchprg
LEXPEND LNCHPRG
LEXPEND 1.000000 -0.192704
LNCHPRG -0.192704 1.000000
student spends more for lexpend than lnchprg. Negative correlation (v)
C3.8
the inclusion of lnchprg suppressed the coefficient on log(expend) (1) when lnchprg increases, math10 decreases; (2) when lexpend increases, lnchprg decreases. Therefore, when lexpend increases, what happens, in total? When lexpend increases, lnchprg decreases, which causes math10 to go . . . up.
(i)
descriptive stat PRPBLCK 0.113486 0.041444 0.981658 0.000000 0.182416 2.700012 10.56841
INCOME 47053.78 46272.00 136529.0 15919.00 13179.29 0.962831 7.551386
Jarque-Bera Probability
1473.100 0.000000
416.2135 0.000000
Sum Sum Sq. Dev.
46.41594 13.57651
19244998 7.09E+10
Observations
409
409
Mean Median Maximum Minimum Std. Dev. Skewness Kurtosis
prpblck = percentage income = dollar (ii) Psoda = 0,956 + 0,115 prpblck + 1,6*10-6
Dependent Variable: PSODA Method: Least Squares Date: 12/20/14 Time: 21:39 Sample: 1 410 Included observations: 401 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C PRPBLCK INCOME
0.956320 0.114988 1.60E-06
0.018992 0.026001 3.62E-07
50.35379 4.422515 4.430130
0.0000 0.0000 0.0000
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.064220 0.059518 0.086115 2.951465 415.7934 13.65691 0.000002
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
1.044863 0.088798 -2.058820 -2.028940 -2.046988 1.696180
The coefficient on prpblck is 0.1149882. The literal interpretation would be: when prpblck increases by 1, the price of a medium soda increases by 11 cents. The only problem is, the notion of increasing prpblck by 1 is not very meaningful. prpblck is the proportion of individuals in a zip code who are black cannot increase by 1 unless the proportion of individuals in a zip code starts out as 0. That is, the only zip code that can increase by 1 is a zip code that starts out with no individuals who are black, and then becomes a zip code that is made up only of individuals who are black. This is not a very useful marginal effect. In order to interpret the marginal effect more usefully, look at smaller (more realistically-sized) changes. For instance, an increase of 0.01 (an increase of 1 in the percentage of individuals who are black in a zip code) is predicted to increase the price of a medium soda by 0.1149882 × 0.01 = 0.00114988,
C4.1 (i) (ii) (iii)
As expenditure of candidate A increases for 1%, percentage of vote for candidate A will increase for B1/100 H0: B1=-B2 or H0: B1+B2=0 1% increases expendA and 1% increases expendB leaves voteA unchanged Estimate model voteA = 45,079 + 6,083 lexpendA – 6,615 lexpendB + 0,152 prtystrA
Dependent Variable: VOTEA Method: Least Squares Date: 12/21/14 Time: 16:52 Sample: 1 173 Included observations: 173 Variable
Coefficient
Std. Error
t-Statistic
Prob.
C LEXPENDA LEXPENDB PRTYSTRA
45.07893 6.083316 -6.615417 0.151957
3.926305 0.382150 0.378820 0.062018
11.48126 15.91866 -17.46321 2.450210
0.0000 0.0000 0.0000 0.0153
R-squared Adjusted R-squared S.E. of regression Sum squared resid Log likelihood F-statistic Prob(F-statistic)
0.792557 0.788874 7.712335 10052.14 -596.8609 215.2266 0.000000
Mean dependent var S.D. dependent var Akaike info criterion Schwarz criterion Hannan-Quinn criter. Durbin-Watson stat
50.50289 16.78476 6.946369 7.019277 6.975948 1.604129
Yes, 1% increases on expend A will probably increase vote for A. 1% increases on expend B will decrease vote for A. (iv)
t-test tB1-B2=(6,083-(-6,615)) / (0,382-0,379)
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