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# Ujian ekonometrika

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### Ujian ekonometrika

1. 1. No Absen 4Nama: Yolandafitri ZulviaNPM: 120420100004Ujian Akhir EkonometrikaJurusan: Magister Ekonomi ManajemenDosen: Nury EffendiHari/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 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 0.2275 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 0.0000R-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) 0.000000Estimation Command:=========================LS LOG(WAGE) C EDUC EXPER EXPER^2Estimation Equation:=========================LOG(WAGE) = C(1) + C(2)*EDUC + C(3)*EXPER + C(4)*EXPER^2Substituted Coefficients:=========================LOG(WAGE) = 0.127997507231 + 0.0903658157891*EDUC + 0.041008875312*EXPER -0.000713558157785*EXPER^2
2. 2. R-squared 0.300273 N 526ii) 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?
3. 3. 2. SOAL C6.5Use the housing price data in HPRICE1.RAW for this exercise.Log(price)= β0+ β1 log(lotsize) + β2 log(sqrft) + β3bdrms + uJawab: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 0.0497 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 0.1831R-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) 0.000000Estimation Command:=========================LS LOG(PRICE) C LLOTSIZE LSQRFT BDRMSEstimation Equation:=========================LOG(PRICE) = C(1) + C(2)*LLOTSIZE + C(3)*LSQRFT + C(4)*BDRMSSubstituted Coefficients:=========================LOG(PRICE) = -1.29704178525 + 0.167966674526*LLOTSIZE + 0.700232436031*LSQRFT +0.0369583833496*BDRMSii)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)= 5.277535 Jadi, lotsize naik 20000 akan meningkatkan price 5.277535
4. 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 =1.05257iii). For explaining variation in price, decide whether you prefer the model from part (i) or the model price= β0+ β1 lotsize + β2 sqrft + β3bdrms + uDependent 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 0.4622 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 0.1279R-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) 0.000000Estimation Command:=========================LS PRICE C LOTSIZE SQRFT BDRMSEstimation Equation:=========================PRICE = C(1) + C(2)*LOTSIZE + C(3)*SQRFT + C(4)*BDRMSSubstituted Coefficients:=========================PRICE = -21.7703086036 + 0.00206770660199*LOTSIZE + 0.122778185222*SQRFT + 13.8525218631*BDRMS
5. 5. 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?JAWABDependent 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 0.0000 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 0.0000R-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 semuanyaBlack orang hitam digaji 19% lebih rendah dibandingkan dengan orang kulit lainnon 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:
6. 6. 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 0.0000 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 0.0912R-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.JAWABDependent 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 0.0000 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. 7. EDUC*BLACK -0.022624 0.020183 -1.120943 0.2626R-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) 0.000000Estimation Command:=========================LS LOG(WAGE) C EDUC EXPER TENURE MARRIED BLACK SOUTH URBAN EDUC*BLACKEstimation 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*BLACKSubstituted 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*BLACKBlack dan educ tidak signifikan.Artinya Pendidikan akan meningkatkan gaji tidak melihat warna kulitanya apakahhitam, 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?
8. 8. JAWABDependent 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 0.0000 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 0.5526R-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) 0.000000Estimation Command:=========================LS LOG(WAGE) C EDUC EXPER TENURE MARRIED BLACK SOUTH URBAN MARRIED*BLACKEstimation 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*BLACKSubstituted 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*BLACKJadi married*black tidak signifikan artinya status pernikahan akan meningkatkangaji tidak melihat warna kulitnya.