计量经济分析（第六版）答案 Midterm01.docx

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1、NAMEEconometrics I, Fall, 2001, Professor W. Greene Midterm Examination.1. Consider random sampling from the normal distribution with mean 日 and variance a2. Xi N|i,o2, i =For a random variable X which is normally distributed, the variable Y = ex has a lognormal distribution with meanEy = +t|Mean of

2、X|+Constant-6.7875247695.0447230-1.345.1806KIDS-1.126731368.45458453-2.479.0143.66000000AGE.3119934568.233749811.335.184142.786667AGESQ-.003957994169.0027314125-1.449.14951901.0267EDUC.4349928716.0836213975.202.000012.640000This is theestimated covariance matrixfor the coefficientestimates.123451125

3、.4492.3275-1.1410.0131-.130221.3275.2066-.0259.0004-.004331-1.1410-.0259.0546-.0006.002241.0131.0004-.0006.00000460614 -.0000253330351-.1302-.0043.0022-.00002533303.0070All families+I Ordinary least squares regression Weighting variable = none |I Dep. var. = WAGE Mean= 3.792050000, SD=2.342414629|I

4、Model size: Observations =150, Parameters =4, Deg.Fr.=146 |I Residuals: Sum of squares= 702.8039766, Std.Dev.=2.19402 |I Fit:R-squared= .140353, Adjusted R-squared =.12269 |I Model test: F3,146 =7.95, Prob value =.00006 |+I Variable|Coefficient|Standard Error|t-ratio|P|T|t|Mean ofX|+4-+Constant-5.00

5、16342585.0801906-.985. 3265AGE.1707522643.23065611.740.460342.786667AGESQ-.001952378674.0026543577-.736.46321901.0267EDUC.4113360731.0845255754.866.000012.640000Families with Children+I Ordinary least squares regression Weighting variable = none |I Dep. var. = WAGE Mean= 3.618494949, S.D.=2.16882762

6、6|I Model size: Observations =99, Parameters =4, Deg.Fr.=95 |I Residuals: Sum of squares= 411.8075395, Std.Dev.=2.08202 |I Fit:R-squared= .106657z Adjusted R-squared =.07845 |I Model test: F 3Z95 =3.78z Prob value =. 01304 |+4-+I Variable|Coefficient|Standard Error|t-ratioIP|T|t|Mean ofX|+Constant-1

7、6.369942018.3220409-1.967.0521AGE.7832912943.401493691.951.054039.707071AGESQ-.009508912588.0049393377-1.925.05721622.6970EDUC.3372655578.113692172.966.003812.797980Families with no chldren+I Ordinary least squares regression Weighting variable = none |I Dep. var. = WAGE Mean= 4.128950980, S.D.=2.63

8、7440831|I Model size: Observations =51z Parameters =4, Deg.Fr.=47 |I Residuals: Sum of squares= 233.2955027, Std.Dev.=2.22794 |I Fit:R-squared= .329234, Adjusted R-squared =.28642 |I Model test: F3,47 =7.69, Prob value =. 00028 |+4-IVariable|Coefficient|Standard Error|t-ratio|P|T|tIMean ofX|+Constant6.4550570909.4372178.684.4973AGE-.3032343154.39863070-.761.450648.764706AGESQ.002569488111.0043602988.589.55852441.3137EDUC.5017377974.128003413.920.000312.333333