In an earlier tutorial, you were introduced to a data set that contains voting information

for 173 US election districts.

The variables are defined as follows:

voteA = percentage of the vote received by Candidate A

expendA =campaign expenditure by Candidate A (in million dollars)

expendB =campaign expenditure by Candidate B (in million dollars)

prtystrA =a measure of party strength for candidate A (the fraction of the most recent presidential vote that went to A's party, expressed in percent)

Use the dataset VoteA2.dta and run a regression where voteA is the dependent variable

against the other variables. Always include a constant (or an intercept) in the regression.

(a) Report the model results. Interpret the coefficients, are they individually significant?

(Note: use a significance level of 5 percent).

(b) Run another regression, this time a regression of voteA on expendB. Report the model

results, are the coefficients significant? (Note: use a significance level of 5 percent).

(c) Use the residuals in (b) to compute the squared residuals and regress the squared

residuals on expendB. Is heteroskedasticity present in the model? Why or why not?

Create the following new variables:

expendA_ D =1 if campaign expenditure by A exceeds 5.5 million dollars, =0 otherwise

expendB_ D =1 if campaign expenditure by B exceeds 5.5 million dollars, =0 otherwise

expendAB_ D = expendA_ D)*expendB_D (i.e., an interaction between expendA_ D and

expendB_ D)

expendB_ 0 =1 if campaign expenditure by B is less than 5 million dollars, =0 otherwise

expendB_ 1=1 if campaign expenditure by B is between 5 and 5.5 million dollars, =0 otherwise

(d) Run a regression where expendA_D and expendB_D are the only regressors. Report the

model results. Interpret the coefficients, are they individually significant? (Note: use a

significance level of 5 percent).

(e) Run another regression, this time add the interaction term expendAB_ D as the third

regressor. Interpret the coefficients, are they individually significant? (Note: use a

significance level of 5 percent).

(0 Run another regression, this time expendB_0 and expendB_1 are the only regressors.

Report the model results, are the coefficients significant? (Note: use a significance level

of 5 percent).

(g) Compare models (d), (e) and (f). Do you think model (0 suffers from omitted variable

bias due to the omission of expendB D? Why or why not?

Here is the link of voteA2 : https://drive.google.com/file/d/1ITLfC4NzMBq26Vo2QWyY2m2LHaP_cNC1/view?usp=sharing

Pls answer these questions academically. And please use Stata to regress these data.