I forgot to ask how to set the constraints that r,g>0 as
negative
returns and rates of growth do not make sense.
Help is highly appreciated
Kind regards
Pascal
> -----Urspr�ngliche Nachricht-----
> Von: [email protected]
> Gesendet: Fr. 15.08.08 (07:51)
> An: [email protected]
> Betreff: st: Programming an iterative regression with
converging
parameters
>
> Hello STATA users,
>
> I am new to STATA and have to replicate a paper by
Easton, Taylor,
> Shroff, Soughiannis (2002) about the implicit calculation
of the
> implied cost of capital r. The problem is that the proxy
for the
> dependent variable X_cT is caluclated with r, while r
itself is to be
>
> estimated from the regression coefficients. They note
that an initial
>
> r=0.12 or 12% is used to calculate X_cT. Then it is used
as dependent
>
> variable to run the first regression. From the resulting
intercept
> and
> regression coefficient the r and growth rate g are
calculated. The
> new
> r is used as revised estimate of the implied cost of
capital r to
> recalculate X_cT. The regression is run again and the
process of
> estimating r from the coefficients, and recalculating
X_cT starts all
>
> over. The logic is simple and straight forward - the
programming for
> a
> rookie is not. Reading about STATA Programming I guess
that a loop
> using the while command is needed, with the iterative
regression to
> stop when the r from the previous run and the new r are
so close as
> not changing matterially. I used r_`j'-r_`i'>0.0001 as
indicator to
> continue with the loop, as the "true" condition. Also r
must be
> positiv, whereas the forth root can be negative, but is
economic
> meaningless. How to program constraints I do not know -
sounds like
> MATA-rogramming then. So I tried it first without the
constraints.
> Maybe the nl command could work.
>
> Here is my do file, and when executing it I even do not
get an error
>
> message, neither results, not anything:
>
> clear
> set memory 50m
> use "C:Usersstock.GESSDesktopIBES
> DatensatzData_replication_study_1_X_cT.dta"
> preserve
> generate r_1=0.12
> generate
> X_cT_1=x1 x2 x3 x4 [(1 r_1)^3-1]*d0 [(1 r_1)^2-1]*d0 [(1
r_1)-1]*d0
> generate depvar=X_cT_1/B0
> local i=1
> forvalues i=1/100 {
> local j=1
> while r_`j'-r_`i'>0.001 {
> replace r_`i'=r_`j'
> replace
> X_cT_`i'=x1 x2 x3 x4 [(1 r_`i')^3-1]*d0 [(1
r_`i')^2-1]*d0 [(1
r_`i')-1]*d0
> replace depvar=X_cT_`i'/B0
> regress depvar indvar
> generate g_`i'=(_b[_cons] 1)^(1/4)-1, replace
> generate r_`j'=(_b[indvar] (_b[cons] 1))^(1/4)-1,
replace
> }
> }
> save Data_replication_study_1_X_cT_1.dta
> describe
> summarize
> estimates save regression_1, replace
> estimates tab
>
> Any suggestions hw to program such a process are highly
appreciated.
>
> Kind regards
>
> Pascal Stock
>
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>
>
>
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