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3. [40 pts Log of wage rate was regressed on the following explanatory variables: log(wage)0.389 ...
? ANSWER FROM LETTER "E" AND DOWNWARDS III- (15pts) You are given the following economic model...
?
ANSWER FROM LETTER "E" AND DOWNWARDS
III- (15pts) You are given the following economic model 0.013 -$26 Rsquare- 0.4177 0.0012ten (0.00024 log(wage) 0.478 + 0.085edu + 0.059ten-0.058/emale-0.01 ledu.female-0.02 1/emale./en- Std errors (0.113) (0.008) (0.007) (0.174) (0.006 With all the variables described as follows: log(wage) -log of average hourly wage; female is a dummy variable equal to 1 if the observed person is a female, and O if make; edu female is an interaction variable equal to education'female; edu is...
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc...
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc + Bztenure + Bzblack + Batenure? + Bseduc. black + Botenure black With all the variables described as follows: In(wage) = natural log of monthly earnings; educ = years of education; tenure = years with current employer; black = 1 if black and 0 if not; tenure? = tenure*tenure educ.black is an interaction variable equal to educ*black; tenure,black is an interaction variable equal to...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer)....
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0....
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
asap i beg u IL. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: log (wage) educ 0.056** 0.062* 0.049* 0.022) (0....
asap i beg u
IL. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: log (wage) educ 0.056** 0.062* 0.049* 0.022) (0.006) (0.022) feduc 0.027 C0.027) 0.021 0.028) married 1.000 0.606 (0.518) (0.466) age 0.032 C0.114) (0.103) (0.116) 0.034 feduc. educ -0.001 (0.002) -0.0003 (0.002) married.age 0.037 0.025 C0.016) (0.014) 0.001 0.001 -0.00000 C0.002) C0.002) C0.002) agesq Constant 5.306**5.218* 5.090*** (1.923) C1.723) C1.932) observations 741 R2 741 0.151 741...
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ANSWER FROM LETTER "E" AND DOWNWARDS
III- (15pts) You are given the following economic model 0.013 -$26 Rsquare- 0.4177 0.0012ten (0.00024 log(wage) 0.478 + 0.085edu + 0.059ten-0.058/emale-0.01 ledu.female-0.02 1/emale./en- Std errors (0.113) (0.008) (0.007) (0.174) (0.006 With all the variables described as follows: log(wage) -log of average hourly wage; female is a dummy variable equal to 1 if the observed person is a female, and O if make; edu female is an interaction variable equal to education'female; edu is...
1. (9 pts) Consider the following economic model describing workers' earnings: In(wage) = Bo + Beduc + Bztenure + Bzblack + Batenure? + Bseduc. black + Botenure black With all the variables described as follows: In(wage) = natural log of monthly earnings; educ = years of education; tenure = years with current employer; black = 1 if black and 0 if not; tenure? = tenure*tenure educ.black is an interaction variable equal to educ*black; tenure,black is an interaction variable equal to...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
III-(15pts) You are given the following estimated equation: log(wage)- 0.18+0.093edu +0.044exp+0.043 female-0.016edu female-0.010exp female-0.00068 exp (0.0001) 0.014) 0.4160 0.003 Std errors (0.132) (0.009) (0.005) (0.196) n-526 R-square With all the variables described as follows: logiwage)-log of average hourly wage: female is a dummy variable equal to 1 if the observed person is a female, and 0 if male; edu female is an interaction variable equal to education 'female; edu is the number of years of schooling exp is the number...
asap i beg u
IL. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: log (wage) educ 0.056** 0.062* 0.049* 0.022) (0.006) (0.022) feduc 0.027 C0.027) 0.021 0.028) married 1.000 0.606 (0.518) (0.466) age 0.032 C0.114) (0.103) (0.116) 0.034 feduc. educ -0.001 (0.002) -0.0003 (0.002) married.age 0.037 0.025 C0.016) (0.014) 0.001 0.001 -0.00000 C0.002) C0.002) C0.002) agesq Constant 5.306**5.218* 5.090*** (1.923) C1.723) C1.932) observations 741 R2 741 0.151 741...
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