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regressionleast-squaresstata
Reginald Green
Reginald Green

Reputation:

Storing coefficients from a Regression in Stata

I am trying to store the coefficients from a simulated regression in a variable b1 and b2 in the code below, but I'm not quite sure how to go about this. I've tried using return scalar b1 = _b[x1] and return scalar b2 = _b[x2], from the rclass() function, but that didn't work. Then I tried using scalar b1 = e(x1) and scalar b2 = e(x2), from the eclass() function and also wasn't successful.

The goal is to use these stored coefficients to estimate some value (say rhat) and test the standard error of rhat.

Here's my code below:

program montecarlo2, eclass  
    clear  
    version 11  
    drop _all  
    set obs 20  
    gen x1 = rchi2(4) - 4  
    gen x2 = (runiform(1,2) + 3.5)^2  
    gen u = 0.3*rnormal(0,25) + 0.7*rnormal(0,5)  
    gen y = 1.3*x1 + 0.7*x2 + 0.5*u  
* OLS Model  
    regress y x1 x2             
    scalar b1 = e(x1)  
    scalar b2 = e(x2)  
end

I want to do something like,

rhat = b1 + b2, and then test the standard error of rhat.

Upvotes: 0

Views: 13363

Answers (1)

Nick Cox
Nick Cox

Reputation: 37208

Let's hack a bit at your program:

Version 1 

program montecarlo2
    clear  
    version 11  
    set obs 20  
    gen x1 = rchi2(4) - 4  
    gen x2 = (runiform(1,2) + 3.5)^2  
    gen u = 0.3*rnormal(0,25) + 0.7*rnormal(0,5)  
    gen y = 1.3*x1 + 0.7*x2 + 0.5*u  
* OLS Model  
    regress y x1 x2             
end

I cut drop _all as unnecessary given the clear. I cut the eclass. One reason for doing that is the regress will leave e-class results in its wake any way. Also, you can if you wish add 

scalar b1 = _b[x1] 
scalar b2 = _b[x2] 
scalar r = b1 + b2

either within the program after the regress or immediately after the program runs.

Version 2 

program montecarlo2, eclass 
    clear  
    version 11  
    set obs 20  
    gen x1 = rchi2(4) - 4  
    gen x2 = (runiform(1,2) + 3.5)^2  
    gen u = 0.3*rnormal(0,25) + 0.7*rnormal(0,5)  
    gen y = 1.3*x1 + 0.7*x2 + 0.5*u  
* OLS Model  
    regress y x1 x2         
    * stuff to add    
end

Again, I cut drop _all as unnecessary given the clear. Now the declaration eclass is double-edged. It gives the programmer scope for their program to save e-class results, but you have to say what they will be. That's the stuff to add indicated by a comment above.

Warning: I've tested none of this. I am not addressing the wider context. @Dimitriy V. Masterov's suggestion of lincom is likely to be a really good idea for whatever your problem is.

Upvotes: 0

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