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a) Interpret the coefficients on EXP, Exp2, and Edu and the Dummy variable. Check whether the dummy variable is statistically significant at the 5% level based on the p-value method.
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Assignment # 4
Retrieve the data for South Korea ( for the Cobb-Douglas Production function) from D2L in Excel and import the data in Eviews. Generate the logs of L,K,and rgd. Note: L=labour, K=capital, rgd = real gdp. In Eviews you will click “quick” and select “generate” option. Example, lY = log (rgd), lk=log(K), ll =log(L).
Then run a multiple regression with the command: ly c ll lk
a) Interpret the coefficients on ll and lk, and adjusted R-square.
b) Examine at the 1% significance level, based on the p-value method, whether the coefficients on ll and lk are significant.
Refer to the regression model in question 1. Formally test at the 1% significance level, based on the F-test ( p-value method) whether the production function shows constant returns to scale.
Note: After running regression for question 1, select “View” and “coefficient diagnostic “ test, and Wald test and then type: C(2) + C(3) =1
Retrieve the data file Slid3 ( from D2l) and download in Excel. In Excel create one data file for men, called Slid(Men) and another called Slid (Women).
The file has the following variables: Age, Gender, Exp (experience), WS ( wages and salaries), and Edu( years of education).
Note: in the original there is a variable called Gender which has a value of 1 for men and 2 for women. To create a file for men, click filter and gender. In the relevant place, choose gender< 2. To create a file for women you will choose >1.
Import the file for men , generate log of ws, called lws and also generate the square of age, called age2, and square of Exp, called EXP2 and then run the following regressions:
Lws c age age2 edu ( for men)
Lws c age age2 edu ( for women)
Compare the results.
Use the same database as for question 3 and run the following regression models.
Lws c Exp Exp2 edu ( for men)
Lws c Exp Exp2 edu ( for women)
Compare the results.
Use the original data file, SLID3. Do not split the file into two files ( for men and women)
Generate a dummy variable, called dummy in Eviews:
Dummy = (gender
Run the following regression model:
Lws c exp exp2 dummy edu
a) Interpret the coefficients on EXP, Exp2, and Edu and the Dummy variable. Check whether the dummy variable is statistically significant at the 5% level based on the p-value method.
b) Write the regression results separately for men and women.
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