Re: st: Constructing a variable from standard deviations

["M.P.J. van Zaal" <Matthias.vanZaal@student.uva.nl>]

Hi mr. Buis,

Thanks for your help. 

However I have a question:

You state that the residual variance is assumed to be constant. This 
is actually not the case. I have 106 different residual stand 
deviations. I achieved this by using "predict "nameocc" 
if "dummyoccupation"==1, resid"  to predict the residuals. Now I have 
106 different residuals, and when i check tabstat their standard 
deviations are quite different (varying from 0.18-0.8). The paper i am 
trying to replicate reports a similar range of standard deviations.

I would like to have a variable with 106 "observations" (the standard 
deviations) so I would like to know how to save these different 
standard deviations in one variable.


Do you think I can construct a variable directly from these standard 
deviations using this method?

Many thanks! 






----- Original Message -----
From: Maarten buis <maartenbuis@yahoo.co.uk>
Date: Monday, November 22, 2010 11:01 am
Subject: Re: st: Constructing a variable from standard deviations
To: statalist@hsphsun2.harvard.edu

> --- On Mon, 22/11/10, M.P.J. van Zaal wrote:
> > The goal of the model is to explain sorting into
> > occupations with individual risk preferences. 
> > 
> > In the first stage I run a Mincerian wage regression with
> > dummies for 106 different occupations. The standard
> > deviations of the residuals of this regression is used as
> > measure for occupation (earnings) risk. 
> > 
> > In the second step I use the standard deviation of the
> > residuals as dependent variable in a regression on individual
> > characteristics including their risk preference.
> 
> In that case the residual variance is assumed to be constant.
> This assumption is often called homoscedasticity. You can 
> turn it into a variable, but that would be a very boring 
> variable an you can't use it as a dependent variable as there
> will be no variance...
> 
> Below is a possible solution. It estimates a linear regression
> model, but it allows for differences in the residual variance
> across groups. In the example below I used repair record, in
> your case you would probably do that by occupation. Since you
> have that many occupations, the estimation often becomes quite
> hard, so specifying good starting values can help a lot. So
> the example also showed how to get those.
> 
> *---------------------------- begin example -----------------------
> // define linear regression model with non-constant error variance
> program drop _all
> program define mynormal_lf
> 	version 11
> 	args lnfj mu ln_sigma
> 	quietly replace `lnfj' = ///
>                ln(normalden($ML_y1, `mu', exp(`ln_sigma')))
> end
> 
> // open data
> sysuse auto, clear
> recode rep78 1/2=3
> 
> // estimate a regular regression, i.e. with constant residual 
variance
> reg mpg weight displacement foreign i.rep78
> 
> // store parameters for starting values
> tempname b0 rmse 
> matrix `b0' = e(b)           // effects in the mu equation
> scalar `rmse' = ln(e(rmse))  // constant of the ln_sigma equation
> 
> // specify the model
> ml model lf mynormal_lf                            /// 
>   (mu: mpg = weight displacement foreign i.rep78) ///
>   (ln_sigma: i.rep78)
> 
> // specify the initial values
> ml init `b0'
> ml init ln_sigma:_cons = `= `rmse' '
> 
> // maximize the likelihood
> ml maximize
> 
> // predict the residual variances
> predict sigma, xb eq(ln_sigma)
> replace sigma = exp(sigma)
> 
> // display the residual variances
> // the residual variance is constant within each category of rep78
> table rep78, c(mean sigma)
> *---------------------------- end example -------------------------
> -
> (For more on examples I sent to the Statalist see: 
> http://www.maartenbuis.nl/example_faq )
> 
> Hope this helps,
> Maarten
> 
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
> 
> http://www.maartenbuis.nl
> --------------------------
> 
> 
>      
> 
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