RE: st: Looping and saving regression outputs

[Nick Cox <n.j.cox@durham.ac.uk>]

Sorry, but that makes no sense to me. 

An observation in Stata is one row, case or record in your data. You can't fit a regression to one observation, any more than you can go 

. sysuse auto, clear
(1978 Automobile Data)

. regress mpg weight in 1
insufficient observations
r(2001);

I imagine you mean something else, but you have to tell us what it is. 

Nick 
n.j.cox@durham.ac.uk 


-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Sebastian Galarza
Sent: 17 May 2012 19:01
To: statalist@hsphsun2.harvard.edu
Subject: Re: st: Looping and saving regression outputs

Nick et al, 

Sorry for the lack of clarity. I will try to be more clear now and in future posts. 

Here goes:

I have a panel dataset with several variables, including (sizef) with around 50,000 observations.
The code that I am running does not contain the cycling/loop because I am unsure how to write it. 

So the first question/problem is that:
I would like to cycle through each observation of sizef and run each of three regressions. 

The second question/problem regards:
Recovering the coefficient vector, r2 and standard errors in a matrix. 

After recovering these values for each observation, I want to create a histogram by regression for each of these values (coefficients, r2 and standard errors) to get a visual representation of how they are distributed. 

I hope this helps clarify my issue. 

On May 17, 2012, at 10:37 AM, Nick Cox wrote:

> I think it's the other way round. What you are doing precisely is unclear and more detailed advice is difficult for that reason. 
> 
> For example: 
> 
> You refer to cycling over variables -sizef- but it is not clear where -sizef- appears in your code. 
> 
> You say you have 51,000 variables, but no Stata allows more than 32,767 variables. Do you mean observations?  
> 
> Ada's very helpful reply is about as detailed as you can expect until you clarify such details. 
> 
> For "STATA" read "Stata". 
> 
> Nick 
> n.j.cox@durham.ac.uk 
> 
> Sebastian Galarza
> 
> Ada Ma et al, 
> 
> Thank you for your response. Can you be a little bit more explicit in the coding of this? I am still far from being a STATA power user and some examples might be helpful. Im looking into the svmat command and its a good starting point. Thanks for your input, 
> 
> On May 17, 2012, at 8:15 AM, Ada Ma wrote:
> 
>> First you can create one matrix for each regression by stacking the r2
>> on to the coefficients.  So say you have Q explanatory variable your
>> row matrix would be 1 x (Q+1+1) (one for the constant one for the r2).
>> You might also consider adding the SEs to your matrix too.  You might
>> want to add an extra item at the top end of your matrix to label your
>> regressions too to save your effort.
>> 
>> Then you can stack all the regression results into one big matrix.
>> 
>> Then after you have done all the loopings use -svmat- to turn your big
>> matrix into variables.
>> 
>> Then finally you -outsheet- those variables into a CSV or save the new
>> variables as a separate Stata dataset.
> 
> 
> On Wed, May 16, 2012 at 6:22 PM, Sebastian Galarza
> 
>>> I want to run the following code and save the beta values and r2 for three different regressions in a matrix such as this:
>>> 
>>> Variable B0a B1a B2a R2a B1b B2b B3b R2b B1c B2c B3c R2c
>>> 1
>>> 2
>>> 3
>>> 
>>> I have a total of 51000 variables (sizef) so that the code I would like to run is:
>>> 
>>> 
>>> *for each variable sizef
>>> 
>>> nl (P_WOR = {b0=0.1}*(1 * exp({b1=0.05}* (ageyear))))
>>> predict a
>>> matrix list e(b)
>>> display e(r2)
>>> 
>>> * save beta values and r2
>>> 
>>> nl log3: P_WOR ageyear
>>> predict b
>>> matrix list e(b)
>>> display e(r2)
>>> 
>>> * save beta values and r2
>>> 
>>> nl gom3: P_WOR ageyear
>>> predict c
>>> matrix list e(b)
>>> display e(r2)
>>> 
>>> * save beta values and r2
>>> 
>>> Any help would be greatly appreciated.

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