II. (11pts) You are given the following estimated equation: log(price) 4.83+0.000347sqrft + 0.0117bdrms-0.056colonial +0.000068srft colonial Std....
IL. (1Ipts) You are given the following estimated equation: log(price) =-0.676 + 0.848 log(sqrft)-0.05 1bdrrns-0.269colonial + 0.098bdrms * colonial Std. Errors (0.693) (0.1003) (0.060) n 88, R-squared -0.5793 (0.216) (0.0636) Where the variables are described as follows: price sarfi the size of the house, in squared feet hdrms the number of bedrooms in the house colonial- if the house has a colonial architectural style, and 0 otherwise. bdrms colonial interaction variable the house price, in $1000 a. Provide an appropriate...
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code to solve this in R, thank you
11. (10 marks) (using dataset: "hpricel", in R: data(hprice1, package-wooldridge')) Use the data to 5 estimate the model where price is the house price measured in thousands of dollars iWrite out the results in equation form. iiWhat is the estimated increase in price for a house with one more bedroom, holding square footage and lot size constant? iii What is the estimated increase in price for...
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...
II. (15pts) You are given the following three estimated models, with all the variables described as in question II Dependent variable: educ 0.118***0.062***0.116** (0.024) (0.006) (0.024) meduc 0.094* C0.029) 0.095*** (0.029) married 0.252*0.247** (0.071) (0.068) 0.025 0.026* 0.020** (0.011) (0.011) (0.009) meduc.educ -0.006 C0.002) 0.006* C0.002) marrted. ten 0.009 (0.000 C0.009) (0.009) 0.0002 -0.0003 0.0003 C0.001) (0.0005) (0.001) Constant 4.694*** 5.597*** 4.932 (0.325) (0.100) (0.324) observations 857 R2 857 857 0.152 0.175 *p<0.1; p<0.05; p<0.01 Note: a. Wha education? Explain....
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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...