Wednesday, July 25, 2012

The Return Command

* In Stata the return command is an essential tool that any aspiring programmer should be aware of.

* Note, I have two lines of comments going on in this code.  Hopefully, it is coherent.
* The comments that are commented out as such /* HELLO I am YOU COMMENT */ are referring
* to a method to identify coefficients when the error is multiplicative.

* Within Stata there are two methods by which commands yeild feedback to the user.

* One method is the visual display.

* Let's start with a little generated data

 set obs 10000
 gen x = rnormal()*10
 gen u = exp(cos(rnormal()*10))
 gen y = 30*x*u  /* Imagine we have a multiplicative error and we want to
                       identify the coefficient 30.  */

* All of the above commands do not give any kind of return except visual ones (this is frustrating to me since sometimes)
* However, there is a lot of commands that do.
 gen add_error = y-30*x
 corr add_error x  /* We can see that that the "addative" error is correlated with the
                      explanatory variable */

* corr is an rclass command the majority of commands return as rclass.
* to see the returns from corr
 return list
* We can target the returns in the form that Stata lists:
 di "We will have difficulty estimating the 30 coefficient on x"
 di "because the addative error is correlated with x at a level = " r(rho)

reg y x   /* This badly fails at identifying the 3 because the error is
             correlated with explanatory variable x */
* Most regression/estimator commands are "eclass" commands and store their returns as ereturns.

* To see those returns:
ereturn list

* These returns are available to be targetted if you would like to store a value as a macro.
global rss_1 = e(rss)
global r2_a  = e(r2_a)

* Matrices can be targetted in a similar fassion
matrix b1 = e(b)
matrix list b1

* Most coefficients are also saved as system variables
global bx = _b[x]

* When you do your next eclass command it will override the stored values from the previous
gen lny = ln(y) /* We may attempt to isolate the coefficient by using the natural log command
                   . This makes ln(y) = 30*x*u = ln(30) + ln(x) + ln(u)                      */
gen lnx = ln(x)

reg lny lnx
 di "The " exp(_b[_cons]) " is closer to 30 than the coefficient in the direct estimation ${bx}"
 /* Note that the only reason this works as well as it does is because u is defined so that
    E(ln(u))=E(ln(exp(rnormal())))=E(rnormal())=0 and because there is not constant in the first equation.
If there were a constant in the first equation then we would have
y=c+bxu -> lny=ln(c+bxu) which does not help us. */

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