No Absen 4Nama: Yolandafitri ZulviaNPM: 120420100004Ujian Akhir Ekonometrika Jurusan: Magister Ekonomi ManajemenDosen: Nury EffendiHari/tgl: Rabu 5 Januari 2011

1. Soal Nomor C6.2 buku WoolridgeUse 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 SquaresDate: 01/05/11 Time: 09:18Sample: 1 526Included observations: 526

Variable Coefficient Std. Error t-Statistic Prob.  

C 0.127998 0.105932 1.208296 0.2275EDUC 0.090366 0.007468 12.10041 0.0000

EXPER 0.041009 0.005197 7.891606 0.0000EXPER^2 -0.000714 0.000116 -6.163888 0.0000

R-squared 0.300273    Mean dependent var 1.623268Adjusted R-squared 0.296251    S.D. dependent var 0.531538S.E. of regression 0.445906    Akaike info criterion 1.230158Sum squared resid 103.7904    Schwarz criterion 1.262594Log likelihood -319.5316    Hannan-Quinn criter. 1.242858F-statistic 74.66829    Durbin-Watson stat 1.785009Prob(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

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 predictedlog(wage)? How many people have more experience in thissample?

2. SOAL C6.5Use 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 SquaresDate: 01/05/11 Time: 09:48Sample: 1 88Included observations: 88

Variable Coefficient Std. Error t-Statistic Prob.  

C -1.297042 0.651284 -1.991516 0.0497LLOTSIZE 0.167967 0.038281 4.387712 0.0000LSQRFT 0.700232 0.092865 7.540305 0.0000BDRMS 0.036958 0.027531 1.342413 0.1831

R-squared 0.642965    Mean dependent var 5.633180Adjusted R-squared 0.630214    S.D. dependent var 0.303573S.E. of regression 0.184603    Akaike info criterion -0.496833Sum squared resid 2.862564    Schwarz criterion -0.384227Log likelihood 25.86065    Hannan-Quinn criter. -0.451467F-statistic 50.42373    Durbin-Watson stat 2.088995Prob(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 predictedvalue of price at the same values of the explanatory variables.

JAWAB: Lotsize = 20000, maka pricenya = 0.167967*ln(20000)= 1.663459

EXP(1.663459)= 5.277535Jadi, lotsize naik 20000 akan meningkatkan price 5.277535

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)=1.05257Jadi, 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 modelprice= β0+ β1 lotsize + β2 sqrft + β3bdrms + u

Dependent Variable: PRICEMethod: Least SquaresDate: 01/05/11 Time: 10:11Sample: 1 88Included observations: 88

Variable Coefficient Std. Error t-Statistic Prob.  

C -21.77031 29.47504 -0.738601 0.4622LOTSIZE 0.002068 0.000642 3.220096 0.0018SQRFT 0.122778 0.013237 9.275093 0.0000BDRMS 13.85252 9.010145 1.537436 0.1279

R-squared 0.672362    Mean dependent var 293.5460Adjusted R-squared 0.660661    S.D. dependent var 102.7134S.E. of regression 59.83348    Akaike info criterion 11.06540Sum squared resid 300723.8    Schwarz criterion 11.17800Log likelihood -482.8775    Hannan-Quinn criter. 11.11076F-statistic 57.46023    Durbin-Watson stat 2.109796Prob(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

3.SOAL C7.2(i) Estimate the modelLog (wage)= β + β1 educ + β2 exper + β3 tenure + β4 married + β5 black + β6 south + β7 urban + uand report the results in the usual form. Holding other factors fixed, whatis the approximate difference in monthly salary between blacks and nonblacks?Is this difference statistically significant?JAWAB

Dependent Variable: LOG(WAGE)Method: Least SquaresDate: 01/05/11 Time: 10:20Sample: 1 935Included observations: 935

Variable Coefficient Std. Error t-Statistic Prob.  

C 5.395497 0.113225 47.65286 0.0000EDUC 0.065431 0.006250 10.46826 0.0000

EXPER 0.014043 0.003185 4.408852 0.0000TENURE 0.011747 0.002453 4.788998 0.0000MARRIED 0.199417 0.039050 5.106691 0.0000

BLACK -0.188350 0.037667 -5.000444 0.0000SOUTH -0.090904 0.026249 -3.463193 0.0006URBAN 0.183912 0.026958 6.822087 0.0000

R-squared 0.252558    Mean dependent var 6.779004Adjusted R-squared 0.246914    S.D. dependent var 0.421144S.E. of regression 0.365471    Akaike info criterion 0.833260Sum squared resid 123.8185    Schwarz criterion 0.874676Log likelihood -381.5490    Hannan-Quinn criter. 0.849052F-statistic 44.74706    Durbin-Watson stat 1.822637Prob(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 theyare jointly insignificant at even the 20% level.

JAWAB:

Dependent Variable: LOG(WAGE)Method: Least SquaresDate: 01/05/11 Time: 10:25Sample: 1 935Included observations: 935

Variable Coefficient Std. Error t-Statistic Prob.  

C 5.358676 0.125914 42.55812 0.0000EDUC 0.064276 0.006311 10.18400 0.0000

EXPER 0.017215 0.012614 1.364747 0.1727TENURE 0.024929 0.008130 3.066433 0.0022MARRIED 0.198547 0.039110 5.076585 0.0000

BLACK -0.190664 0.037701 -5.057240 0.0000SOUTH -0.091215 0.026236 -3.476774 0.0005URBAN 0.185424 0.026959 6.878122 0.0000

EXPER^2 -0.000114 0.000532 -0.213964 0.8306TENURE^2 -0.000796 0.000471 -1.690923 0.0912

R-squared 0.254958    Mean dependent var 6.779004Adjusted R-squared 0.247709    S.D. dependent var 0.421144S.E. of regression 0.365278    Akaike info criterion 0.834322Sum squared resid 123.4210    Schwarz criterion 0.886092Log likelihood -380.0455    Hannan-Quinn criter. 0.854062F-statistic 35.17112    Durbin-Watson stat 1.819339Prob(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 todepend on race and test whether the return to education does dependon race.JAWAB

Dependent Variable: LOG(WAGE)Method: Least SquaresDate: 01/05/11 Time: 10:34Sample: 1 935Included observations: 935

Variable Coefficient Std. Error t-Statistic Prob.  

C 5.374817 0.114703 46.85866 0.0000EDUC 0.067115 0.006428 10.44160 0.0000

EXPER 0.013826 0.003191 4.333276 0.0000TENURE 0.011787 0.002453 4.805362 0.0000MARRIED 0.198908 0.039047 5.094007 0.0000

BLACK 0.094809 0.255399 0.371217 0.7106SOUTH -0.089450 0.026277 -3.404111 0.0007URBAN 0.183852 0.026955 6.820800 0.0000

EDUC*BLACK -0.022624 0.020183 -1.120943 0.2626

R-squared 0.253571    Mean dependent var 6.779004Adjusted R-squared 0.247122    S.D. dependent var 0.421144S.E. of regression 0.365420    Akaike info criterion 0.834043Sum squared resid 123.6507    Schwarz criterion 0.880636Log likelihood -380.9150    Hannan-Quinn criter. 0.851809F-statistic 39.32158    Durbin-Watson stat 1.826713Prob(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 differacross four groups of people: married and black, married and nonblack,single and black, and single and nonblack. What is the estimated wagedifferential between married blacks and married nonblacks?

JAWAB

Dependent Variable: LOG(WAGE)Method: Least SquaresDate: 01/05/11 Time: 10:38Sample: 1 935Included observations: 935

Variable Coefficient Std. Error t-Statistic Prob.  

C 5.403793 0.114122 47.35093 0.0000EDUC 0.065475 0.006253 10.47095 0.0000

EXPER 0.014146 0.003191 4.433117 0.0000TENURE 0.011663 0.002458 4.744941 0.0000MARRIED 0.188915 0.042878 4.405892 0.0000

BLACK -0.240820 0.096023 -2.507943 0.0123SOUTH -0.091989 0.026321 -3.494879 0.0005URBAN 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 6.779004Adjusted R-squared 0.246388    S.D. dependent var 0.421144S.E. of regression 0.365599    Akaike info criterion 0.835018Sum squared resid 123.7714    Schwarz criterion 0.881611Log likelihood -381.3708    Hannan-Quinn criter. 0.852784F-statistic 39.17047    Durbin-Watson stat 1.824148Prob(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.