I wanted to ask the most appropriate method for finding the best polynomial model for multivariate regression.
So far, I have been working with single covariate regressions and used the following code to generate a table showing the R2, AIC and BIC.
Code:
foreach i of numlist 1/5 { if `i' != 1{ gen x`i' = x^`i' } qui reg y x* estimate store M_'i' qui estat ic matrix M_IC_`i' = r(S) scalar aic_`i' = M_IC_`i'[1, 5] scalar bic_`i' = M_IC_`i'[1, 6] } estout M_*, cells(b(star fmt(3)) se(par fmt(3))) stats(r2 r2_a aic bic df_r, labels(R^2 aR^2 AIC BIC df))
For reference, I am using the heterogeneous local average treatment effect (HLATE) proposed by Becker et al. (2012) in the context of RDD.
Code:
reg AR GDP_dev* ceqi* T TxGDP* Txceqi*
The first is more theoretical and probably very elementary: the degree of the covariates functions have to be all the same, or can I have a regression with x12 and x23?
The second question concerns the use of STATA. Is there a way to test which model is the best, using code similar to the one posted earlier in the message, or should I use another approach altogether?
I apologize if the question is not precise or I used imprecise terms. I hope I have made myself clear.
edit: I can't change the title but most likely I am referring to a multivariable regression modeling, not multivariate.
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