1. No Absen 4
Nama: Yolandafitri Zulvia
NPM: 120420100004
Ujian Akhir Ekonometrika
Jurusan: Magister Ekonomi Manajemen
Dosen: Nury Effendi
Hari/tgl: Rabu 5 Januari 2011
1. Soal Nomor C6.2 buku Woolridge
Use the data in WAGE1.RAW for this exercise.
(i) Use OLS to estimate the equation
Log(wage) βo+β1educ+ β2exper+ β3exper2
and report the results using the usual format.
JAWAB:
Dependent Variable: LOG(WAGE)
Method: Least Squares
Date: 01/05/11 Time: 09:18
Sample: 1 526
Included observations: 526
Variable Coefficient Std. Error t-Statistic Prob.
C 0.127998 0.105932 1.208296 0.2275
EDUC 0.090366 0.007468 12.10041 0.0000
EXPER 0.041009 0.005197 7.891606 0.0000
EXPER^2 -0.000714 0.000116 -6.163888 0.0000
R-squared 0.300273 Mean dependent var 1.623268
Adjusted R-squared 0.296251 S.D. dependent var 0.531538
S.E. of regression 0.445906 Akaike info criterion 1.230158
Sum squared resid 103.7904 Schwarz criterion 1.262594
Log likelihood -319.5316 Hannan-Quinn criter. 1.242858
F-statistic 74.66829 Durbin-Watson stat 1.785009
Prob(F-statistic) 0.000000
Estimation Command:
=========================
LS LOG(WAGE) C EDUC EXPER EXPER^2
Estimation Equation:
=========================
LOG(WAGE) = C(1) + C(2)*EDUC + C(3)*EXPER + C(4)*EXPER^2
Substituted Coefficients:
=========================
LOG(WAGE) = 0.127997507231 + 0.0903658157891*EDUC + 0.041008875312*EXPER -
0.000713558157785*EXPER^2

2. R-squared 0.300273 N 526
ii) Is exper2 statistically significant at the 1% level?
Jawab:Exper2 significant dilihat dari probabilitasnya 0.0000 tapi dia memiliki
koefisien (-) yaitu -0.000714 artinya dia mempunyai pengaruh negative
terhadap wage. Kalau exper (+) artinya semakin banyak experience atau
pengalaman akan meningkatkan gaji.
iii)
IV) At what value of exper does additional experience actually lower predicted
log(wage)? How many people have more experience in this
sample?

3. 2. SOAL C6.5
Use the housing price data in HPRICE1.RAW for this exercise.
Log(price)= β0+ β1 log(lotsize) + β2 log(sqrft) + β3bdrms + u
Jawab:
Dependent Variable: LOG(PRICE)
Method: Least Squares
Date: 01/05/11 Time: 09:48
Sample: 1 88
Included observations: 88
Variable Coefficient Std. Error t-Statistic Prob.
C -1.297042 0.651284 -1.991516 0.0497
LLOTSIZE 0.167967 0.038281 4.387712 0.0000
LSQRFT 0.700232 0.092865 7.540305 0.0000
BDRMS 0.036958 0.027531 1.342413 0.1831
R-squared 0.642965 Mean dependent var 5.633180
Adjusted R-squared 0.630214 S.D. dependent var 0.303573
S.E. of regression 0.184603 Akaike info criterion -0.496833
Sum squared resid 2.862564 Schwarz criterion -0.384227
Log likelihood 25.86065 Hannan-Quinn criter. -0.451467
F-statistic 50.42373 Durbin-Watson stat 2.088995
Prob(F-statistic) 0.000000
Estimation Command:
=========================
LS LOG(PRICE) C LLOTSIZE LSQRFT BDRMS
Estimation Equation:
=========================
LOG(PRICE) = C(1) + C(2)*LLOTSIZE + C(3)*LSQRFT + C(4)*BDRMS
Substituted Coefficients:
=========================
LOG(PRICE) = -1.29704178525 + 0.167966674526*LLOTSIZE + 0.700232436031*LSQRFT +
0.0369583833496*BDRMS
ii)Find the predicted value of log( price), when lotsize _ 20,000, sqrft _
2,500, and bdrms _ 4. Using the methods in Section 6.4, find the predicted
value of price at the same values of the explanatory variables.
JAWAB:
• Lotsize = 20000, maka pricenya = 0.167967*ln(20000)= 1.663459
EXP(1.663459)= 5.277535
Jadi, lotsize naik 20000 akan meningkatkan price 5.277535

4. • Sqrft= 2500, maka pricenya = 0.700232*ln(2500)= 5.478647
EXP(5.478647)= 239.522
Jadi, sqrft naik 2500 akan meningkatkan price = 239.522
• Bdrms= 4, maka pricenya= 0.036958*ln(4)= 0.051235
EXP(0.051235)=1.05257
Jadi, bdrms naik 4 akan meningkatkan price =1.05257
iii). For explaining variation in price, decide whether you prefer the model from
part (i) or the model
price= β0+ β1 lotsize + β2 sqrft + β3bdrms + u
Dependent Variable: PRICE
Method: Least Squares
Date: 01/05/11 Time: 10:11
Sample: 1 88
Included observations: 88
Variable Coefficient Std. Error t-Statistic Prob.
C -21.77031 29.47504 -0.738601 0.4622
LOTSIZE 0.002068 0.000642 3.220096 0.0018
SQRFT 0.122778 0.013237 9.275093 0.0000
BDRMS 13.85252 9.010145 1.537436 0.1279
R-squared 0.672362 Mean dependent var 293.5460
Adjusted R-squared 0.660661 S.D. dependent var 102.7134
S.E. of regression 59.83348 Akaike info criterion 11.06540
Sum squared resid 300723.8 Schwarz criterion 11.17800
Log likelihood -482.8775 Hannan-Quinn criter. 11.11076
F-statistic 57.46023 Durbin-Watson stat 2.109796
Prob(F-statistic) 0.000000
Estimation Command:
=========================
LS PRICE C LOTSIZE SQRFT BDRMS
Estimation Equation:
=========================
PRICE = C(1) + C(2)*LOTSIZE + C(3)*SQRFT + C(4)*BDRMS
Substituted Coefficients:
=========================
PRICE = -21.7703086036 + 0.00206770660199*LOTSIZE + 0.122778185222*SQRFT + 13.8525218631*BDRMS

5. 3.SOAL C7.2
(i) Estimate the model
Log (wage)= β + β1 educ + β2 exper + β3 tenure + β4 married + β5 black + β6 south +
β7 urban + u
and report the results in the usual form. Holding other factors fixed, what
is the approximate difference in monthly salary between blacks and nonblacks?
Is this difference statistically significant?
JAWAB
Dependent Variable: LOG(WAGE)
Method: Least Squares
Date: 01/05/11 Time: 10:20
Sample: 1 935
Included observations: 935
Variable Coefficient Std. Error t-Statistic Prob.
C 5.395497 0.113225 47.65286 0.0000
EDUC 0.065431 0.006250 10.46826 0.0000
EXPER 0.014043 0.003185 4.408852 0.0000
TENURE 0.011747 0.002453 4.788998 0.0000
MARRIED 0.199417 0.039050 5.106691 0.0000
BLACK -0.188350 0.037667 -5.000444 0.0000
SOUTH -0.090904 0.026249 -3.463193 0.0006
URBAN 0.183912 0.026958 6.822087 0.0000
R-squared 0.252558 Mean dependent var 6.779004
Adjusted R-squared 0.246914 S.D. dependent var 0.421144
S.E. of regression 0.365471 Akaike info criterion 0.833260
Sum squared resid 123.8185 Schwarz criterion 0.874676
Log likelihood -381.5490 Hannan-Quinn criter. 0.849052
F-statistic 44.74706 Durbin-Watson stat 1.822637
Prob(F-statistic) 0.000000
(i) Sig  signifikan semuanya
Black orang hitam digaji 19% lebih rendah dibandingkan dengan orang kulit lain
non black (putih)
Selatan  orang selatan digaji 9% lebih rendah
(ii) Add the variables exper2 and tenure2 to the equation and show that they
are jointly insignificant at even the 20% level.
JAWAB:

6. Dependent Variable: LOG(WAGE)
Method: Least Squares
Date: 01/05/11 Time: 10:25
Sample: 1 935
Included observations: 935
Variable Coefficient Std. Error t-Statistic Prob.
C 5.358676 0.125914 42.55812 0.0000
EDUC 0.064276 0.006311 10.18400 0.0000
EXPER 0.017215 0.012614 1.364747 0.1727
TENURE 0.024929 0.008130 3.066433 0.0022
MARRIED 0.198547 0.039110 5.076585 0.0000
BLACK -0.190664 0.037701 -5.057240 0.0000
SOUTH -0.091215 0.026236 -3.476774 0.0005
URBAN 0.185424 0.026959 6.878122 0.0000
EXPER^2 -0.000114 0.000532 -0.213964 0.8306
TENURE^2 -0.000796 0.000471 -1.690923 0.0912
R-squared 0.254958 Mean dependent var 6.779004
Adjusted R-squared 0.247709 S.D. dependent var 0.421144
S.E. of regression 0.365278 Akaike info criterion 0.834322
Sum squared resid 123.4210 Schwarz criterion 0.886092
Log likelihood -380.0455 Hannan-Quinn criter. 0.854062
F-statistic 35.17112 Durbin-Watson stat 1.819339
Prob(F-statistic) 0.000000
• Exper (+) semakin banyak experience semakin meningkat gajinya.
Koefisien pada tenure2 (-)
Koefisien pada exper2 (-)
(iii) Extend the original model to allow the return to education to
depend on race and test whether the return to education does depend
on race.
JAWAB
Dependent Variable: LOG(WAGE)
Method: Least Squares
Date: 01/05/11 Time: 10:34
Sample: 1 935
Included observations: 935
Variable Coefficient Std. Error t-Statistic Prob.
C 5.374817 0.114703 46.85866 0.0000
EDUC 0.067115 0.006428 10.44160 0.0000
EXPER 0.013826 0.003191 4.333276 0.0000
TENURE 0.011787 0.002453 4.805362 0.0000
MARRIED 0.198908 0.039047 5.094007 0.0000
BLACK 0.094809 0.255399 0.371217 0.7106
SOUTH -0.089450 0.026277 -3.404111 0.0007
URBAN 0.183852 0.026955 6.820800 0.0000

7. EDUC*BLACK -0.022624 0.020183 -1.120943 0.2626
R-squared 0.253571 Mean dependent var 6.779004
Adjusted R-squared 0.247122 S.D. dependent var 0.421144
S.E. of regression 0.365420 Akaike info criterion 0.834043
Sum squared resid 123.6507 Schwarz criterion 0.880636
Log likelihood -380.9150 Hannan-Quinn criter. 0.851809
F-statistic 39.32158 Durbin-Watson stat 1.826713
Prob(F-statistic) 0.000000
Estimation Command:
=========================
LS LOG(WAGE) C EDUC EXPER TENURE MARRIED BLACK SOUTH URBAN EDUC*BLACK
Estimation Equation:
=========================
LOG(WAGE) = C(1) + C(2)*EDUC + C(3)*EXPER + C(4)*TENURE + C(5)*MARRIED + C(6)*BLACK + C(7)*SOUTH
+ C(8)*URBAN + C(9)*EDUC*BLACK
Substituted Coefficients:
=========================
LOG(WAGE) = 5.37481703029 + 0.0671153308552*EDUC + 0.0138258814311*EXPER +
0.0117870227642*TENURE + 0.198907694212*MARRIED + 0.0948086755005*BLACK - 0.0894495435054*SOUTH
+ 0.183852289256*URBAN - 0.0226236090572*EDUC*BLACK
Black dan educ tidak signifikan.
Artinya Pendidikan akan meningkatkan gaji tidak melihat warna kulitanya apakah
hitam, latin atau asia.
(IV) Again, start with the original model, but now allow wages to differ
across four groups of people: married and black, married and nonblack,
single and black, and single and nonblack. What is the estimated wage
differential between married blacks and married nonblacks?

8. JAWAB
Dependent Variable: LOG(WAGE)
Method: Least Squares
Date: 01/05/11 Time: 10:38
Sample: 1 935
Included observations: 935
Variable Coefficient Std. Error t-Statistic Prob.
C 5.403793 0.114122 47.35093 0.0000
EDUC 0.065475 0.006253 10.47095 0.0000
EXPER 0.014146 0.003191 4.433117 0.0000
TENURE 0.011663 0.002458 4.744941 0.0000
MARRIED 0.188915 0.042878 4.405892 0.0000
BLACK -0.240820 0.096023 -2.507943 0.0123
SOUTH -0.091989 0.026321 -3.494879 0.0005
URBAN 0.184350 0.026978 6.833394 0.0000
MARRIED*BLACK 0.061354 0.103275 0.594083 0.5526
R-squared 0.252842 Mean dependent var 6.779004
Adjusted R-squared 0.246388 S.D. dependent var 0.421144
S.E. of regression 0.365599 Akaike info criterion 0.835018
Sum squared resid 123.7714 Schwarz criterion 0.881611
Log likelihood -381.3708 Hannan-Quinn criter. 0.852784
F-statistic 39.17047 Durbin-Watson stat 1.824148
Prob(F-statistic) 0.000000
Estimation Command:
=========================
LS LOG(WAGE) C EDUC EXPER TENURE MARRIED BLACK SOUTH URBAN MARRIED*BLACK
Estimation Equation:
=========================
LOG(WAGE) = C(1) + C(2)*EDUC + C(3)*EXPER + C(4)*TENURE + C(5)*MARRIED + C(6)*BLACK + C(7)*SOUTH
+ C(8)*URBAN + C(9)*MARRIED*BLACK
Substituted Coefficients:
=========================
LOG(WAGE) = 5.40379326745 + 0.065475113325*EDUC + 0.0141462059065*EXPER +
0.0116628070316*TENURE + 0.188914701141*MARRIED - 0.240819977672*BLACK - 0.0919894174516*SOUTH +
0.184350063352*URBAN + 0.0613536984779*MARRIED*BLACK
Jadi married*black tidak signifikan artinya status pernikahan akan meningkatkan
gaji tidak melihat warna kulitnya.