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.

Assignment # 4

 

 

1.   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.

1. 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

 

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.

 

1. 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.

1. 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.