2SLS regression model

I have a query about instrumental variable (IV) and 2SLS (actually three stage) regression. I am trying to estimate effect of core team(a group of 5 students who work together in first semester of MBA) on the performance of MBA grads (performance, the dependent variable is salary after graduation).

In the first stage, I regresses Fall semester GPA (Y1) on team id, GMAT score , International student (considering that international students grapple with adjustment factors), subject of graduate degree and years of experience.

In the second stage, I regressed cumulative GPA (Y2)on predicted value of Fall semester GPA from previous regression and tracks (i.e. if student has taken marketing specialization or finance specialization).

Finally, I regress log of salary on predicted value of Y2 (Y2hat) and employment stream (Finance, software or consulting).

I have two questions:
a) Is it a correct way to approach?

b) In first regression, team id has P value of 0.084 and t stat of 1.73. Can it be considered significant? Further, R^2 value is .1846, though altogether Prob > F = 0.0000.

Thanks in advance.
Well What i understand over here is

Y is your salary factor
X are your on team id, GMAT score (Continuous data), International student Discrete Data , subject of graduate degree Discrete Data and years of experience (Continuous data)

you can use chi sqr for discrete and regression for continuous

you can see the P value supports the alternate hypothesis (as per the convention i can say ). i.e. you must have taken
null hypo = no dependency on factors
alt hypo = dependency on factors

here your P value is greater than 0.05 that means relation ship is there

your R sqr value is ~ 18.5% that means the given factors are not significantly related to salary / offer (Y) factor

There needs to be some other facts which are influencing the results.
The discrete and continuous data needs to be studied separately.
Thanks, Prince. Would you please clarify one more thing? As some data is in deciles (salary) and some in quintile (GMAT score), will there be any other way to approach?
A MA of 7 is really high unless you have seasonality (and that would be an unusual lag to have seasonality on since it usually occurs at 12/3 or 4). Most ARMA models have 3 or less - in part because you can commonly model a high MA as a lower order AR process. Note that certain AR processes, negative autocorrelation I believe, show up as a sine wave.

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