Reference no: EM132069453

After reviewing the Stata data, the current political climate, I was interested to identify a relationship (if any) between federal spending on border security and vote choice, the premise being that republicans and democrats view border security differently.

Run the initial linear probability model regression:

reg who2012 fedspend_bordercongress_therm male fedspend_terrorismfedspend_schools independent republican, r

Based on these results, it looks like there is a positive increase in the probability of voting for Obama (democrat) as the value of fedspend_border increases, which actually corresponds to a decrease in spending. However, the magnitude is small.

To test an LPM predicted value, we'll use the following characteristics:
Male, independent, rates Congress at 50, believes terrorism spending and school spending should remain constant, and believes border security spending should be increased a great deal
To find the z value:
scalar z = _b[_cons] + _b[fedspend_border]*1 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z
This particular example gives us the percentage probability that the voter will select Obama.
Now we can find the probability of someone with similar characteristics, BUT a change in his opinion of border spending, in this case, someone who believes spending should remain the same (fedspend_border = 4):
scalar z = _b[_cons] + _b[fedspend_border]*4 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z
Now we can find the probability of someone with similar characteristics, BUT a change in his opinion of border spending, in this case, someone who believes spending should decrease a great deal (fedspend_border = 7):
scalar z = _b[_cons] + _b[fedspend_border]*7 + _b[congress_therm]*50 + _b[male]*1 + _b[fedspend_terrorism]*4 + _b[fedspend_schools]*4 + _b[independent]*1 + _b[republican]*0
display z

These are all of our Linear Probability Model values.

Now, let's determine the probit model results:

probit who2012 fedspend_bordercongress_therm male fedspend_terrorismfedspend_schools independent republican, r

We can now use the same Stata scalar command, however, to determine the probit value, we must use:
display normprob(z)

How do we determine the rest of the relevant results and what does the final table look like?