Re: st: Clarify, tfunc(exp)

[Nick Cox <njcoxstata@gmail.com>]

A reference missing here is http://gking.harvard.edu/clarify or
http://www.stanford.edu/~tomz/software/software.shtml

These references point to rather extensive documentation.

I have never used -simqi- but the last fragment of output below does
flag that -simqi- is reporting E[exp()] so why you are uncertain about
that is unclear. As you know exp(E[]) will in general differ.

I wouldn't expect authoritative support on -clarify- from this list,
as people who understand this kind of thing and do it a lot (not me)
appear to use Stata's official commands instead, while I don't think
that the authors are members of this list.

I suspect that you are best advised to write to the program authors
directly, meaning I guess Michael Tomz, if this thread does not
satisfy.

Nick


On Fri, Jan 13, 2012 at 8:36 AM, Renzo Carriero <renzo.carriero@unito.it> wrote:
> Dear Statalist users,
>
> I have a question about the tfunc(exp) option of simqi command in King's et
> al. Clarify command suite. I can't understand how this option exactly
> compute exponentiated expected value. My depvar is a log ratio, say
> y=ln(a/b). My linear regression model contains a 4-category nominal variable
> (indicated by 3 dummies) plus covariates. When I compute mean predicted
> values on the log scale, results yielded by simqi coincide with those
> yielded by Stata's command adjust. On the contrary, when I ask for expected
> values in the original scale, that is exp(y), they markedly differ. It seems
> that Stata (adjust command with the "exp" option) exponentiates the mean
> expected value, such as exp[E(y|x)], while simqi does not. So what is
> exactly the way in which simqi transforms expected values in the original
> scale?
> To help, I add below a simplified example of what I mean using Stata's auto
> dataset
>
> Many thanks
> Renzo
>
> --
> Renzo Carriero
> Dipartimento di Scienze Sociali
> via S. Ottavio 50
> 10124 Torino - Italy
> +390116702658 (office)
> +393898160069 (mobile)
> +390116702612 (fax)
>
> sysuse auto.dta
> g lny=ln(price/mpg)/*generate a log var similar to mine*/
> xtile x=length, nquantile(4) /*generate a 4 category variable from length
> variable"
> tab x, gen(x)/*generate 4 dummies*/
> reg lny x2-x4/*x1 is omitted as reference  category*/
>
>      Source |       SS       df       MS                      Number of obs
> =      74
> -------------+------------------------------                       F(  3,
>  70) =   18,48
>       Model |  10,7554461     3  3,58514872           Prob>  F      =
>  0,0000
>    Residual |   13,580584    70  ,194008343           R-squared     =
>  0,4420
> -------------+------------------------------           Adj R-squared =
>  0,4180
>       Total |  24,3360302    73  ,333370277           Root MSE      =
>  ,44046
>
> ------------------------------------------------------------------------------
>         lny |      Coef.   Std. Err.      t    P>|t|     [95% Conf.
> Interval]
> -------------+----------------------------------------------------------------
>          x2 |    ,576453   ,1461643     3,94   0,000     ,2849374
>  ,8679685
>          x3 |    ,635685   ,1376187     4,62   0,000     ,3612132
>  ,9101569
>          x4 |   1,050355   ,1437037     7,31   0,000     ,7637466
>  1,336962
>       _cons |   5,078354   ,0961171    52,84   0,000     4,886655
>  5,270054
> ------------------------------------------------------------------------------
>
> adjust x2=0 x3=0 x4=0 /*compute expected value for omitted category 1*/
>
>     Dependent variable: lny     Command: regress
> Covariates set to value: x2 = 0, x3 = 0, x4 = 0
> ------------------------------------------------------------------------------------------------------
>
> ----------------------
>      All |         xb
> ----------+-----------
>          |    5,07835
> ----------------------
>     Key:  xb  =  Linear Prediction
>
>
> adjust x2=0 x3=0 x4=0 , exp /*compute expected value for category 1 in the
> original scale (price/mpg)*/
>
>     Dependent variable: lny     Command: regress
> Covariates set to value: x2 = 0, x3 = 0, x4 = 0
> ------------------------------------------------------------------------------------------------------
>
> ----------------------
>      All |    exp(xb)
> ----------+-----------
>          | *160,51 *
> ----------------------
>     Key:  exp(xb)  =  exp(xb)
>
> estsimp reg lny x2-x4/*replicate analysis with estsimp*/
> setx 0 /*set x1=1*/
> simqi
> *this output omitted, it is roughly equal to that of adjust without exp
> option
> simqi, tfunc(exp)
>
>      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
> ---------------------------+--------------------------------------------------
>               E[exp(lny)] | *177,6523 *17,95269     145,2497     215,657
>

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