Subject Code : ECOM20001
Assessment Task:
ECOM20001 Econometrics Assessment Answer

 

Questions

1. Create a new variable called log_birthweight, which is computed as log_birthweight= log(birth weight). Run the following 4 regressions where log_birthweight is the dependent variable, and where each regression includes a constant and one of the following four sets of regressors:

• Reg (1): smoker
• Reg (2): smoker, alcohol, drinks, gambles
• Reg (3): smoker, alcohol, drinks, gambles, nprevisit, tripre1, tripre2, tripre3
• Reg (4): smoker, alcohol, drinks, gambles, nprevisit, tripre1, tripre2, tripre3, unmarried, educ, age

Report the results for Reg (1) - (4) in a table using stargazer() in R. In each regression, and for the remainder of the assignment, work under the assumption of heteroskedastic standard errors. In Reg (4), interpret the coefficient on smoker, and comment on whether it is statistically significantly different from 0 at the 5% level.

2. Using the ggplot() command in R, produce a scatter plot where log_birthweight is on the vertical axis, and nprevisit is on the horizontal axis. In your scatter plot, present a quadratic regression line that highlights any potential nonlinearities in the relationship between the two variables. Does the relationship appear to be nonlinear?

3. Construct a new variable named nprevisit_sq, which is the square of nprevisit_sq. That is, nprevisit_sq = nprevisit X nprevisit. Run another regression where log_birthweight is the dependent variable, and where the regression includes a constant and the following regressors:

• Reg (5): smoker, alcohol, drinks, gambles, nprevisit, nprevisit_sq, tripre1, tripre2, tripre3, unmarried, educ, age

Do the coefficients on nprevisit and nprevisit_sq correspond to the relationship you saw in the scatter plot in question 2?

4. Report estimates and 95% confidence intervals of the following two partial effects from Reg (5), holding all other variables fixed:

• Changing nprevisit from 2 to 4.
• Changing nprevisit from 12 to 14

Briefly explain why you obtain differences in these partial effects, despite the change in nprevisit being 2 in each case.

5. Construct a variable called log_nprevisit, which is computed as log_nprevisit= log(1+nprevisit). Construct a scatter plot using the ggplot() 3 command in R with log_birthweight on the vertical axis and log_nprevisit on the horizontal axis. In your scatter plot, present a quadratic regression line that highlights any potential nonlinearities in the relationship.

6. Generate another variable called log_nprevisit_sq, which is the square of log_nprevisit: log_nprevisit_sq = log_nprevisit X log_nprevisit. Run the following regressions where log_birthweight is the dependent variable, and where each regression includes a constant and one of the following 5 sets of regressors:

• Reg (1): smoker
• Reg (2): smoker, alcohol, drinks, gambles
• Reg (3): smoker, alcohol, drinks, gambles, log_nprevisit, tripre1, tripre2, tripre3
• Reg (4): smoker, alcohol, drinks, gambles, log_nprevisit, tripre1, tripre2, tripre3, unmarried, educ, age
• Reg (5): smoker, alcohol, drinks, gambles, log_nprevisit, log_nprevisit_sq, tripre1, tripre2, tripre3, unmarried, educ, age

Report the results for Reg (1) - (5) in a table using stargazer() in R. Interpret the 4 coefficient estimate on log_nprevisit in Reg (4), and comment on whether it is statistically significant at the 5% level. Also briefly explain why you think there is such a large change in coefficient and standard error on log_nprevisit between Reg (4) and Reg (5) in light of your findings from question 5.

7. Construct one final variable, log_nprevisit_age, which is the following interaction variable between the log of prenatal visits and a mother’s age: log_nprevisit_age = log_nprevisit X age. Run another regression where log_birthweight is the dependent variable, and where the regression includes a constant and the following regressors:

• Reg (6): smoker, alcohol, drinks, gambles, log_nprevisit, log_nprevisit_age, tripre1, tripre2, tripre3, unmarried, educ, age

Based on your regression results, is the elasticity of birthweight with respect to nprevisit larger or smaller in magnitude for older mothers?

8. Based on your estimates for Reg (6) compute the elasticity of birthweight with respect to nprevisit for a mother with age=20 and age=40. Also report the 95% CI for each elasticity.

 

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  • Uploaded By : Mitchell Lee
  • Posted on : December 18th, 2018

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