-clad- is written by Dean Jolliffe, Bohdan Krushelnytskyy, and Anastassia
Semykina (see http://users.starpower.net/djolliffe/cv/cv.htm for his
"Censored Least Absolute Deviations Estimator: CLAD." Stata Technical
Bulletin,).
Yes, the problem is that -clad- is not handling collinear variables very
gracefully. The collinear variables are dropped when -qreg- is used but
then the original varlist is passed to -postfile- which complains that not
all the variables are present. -set trace on- will show you what is
happening as -clad- runs. You could use -_rmcoll- to remove the collinear
variables before call -clad-.
An alternative program (rough draft), -cqreg2- is presented below; it makes
use of Joao Pedro Azevedo's very useful -grqreg-.
Two good introductions to quantile regression are
Koenker, R. and K. Hallock, (2001) "Quantile Regression", Journal of
Economic Perspectives, 15, 143-156.
Cade, B. and B. Noon, (2003) "A Gentle Introduction to Quantile Regression
for Ecologists". Frontiers in Ecology and the Environment, 1, 412-420.
Both of these papers are available Roger Koenker's site
http://www.econ.uiuc.edu/~roger/research/rq/rq.html
Scott
---------------------------------------------------------
*!1.0.0 June 1, 2006 Scott Merryman
program cqreg2
version 9.2
syntax varlist(min=1) [if] [, Quantile(real 0.5) ll(real 0) bs
reps(integer 100) save(string) grqreg]
tempvar yhat
tokenize `varlist'
local lhs "`1'"
mac shift
local rhs "`*'"
preserve
qui {
qreg `lhs' `rhs' `if', q(`quantile')
predict `yhat'
sum `hat'
tempvar tag
while r(min) < `ll' {
keep if `yhat' >= `ll'
qui qreg `lhs' `rhs' `if', quantile(`quantile' )
qui drop `yhat'
predict `yhat' if e(sample)
qui sum `yhat' if e(sample)
}
}
if "`bs'" == "bs" {
bsqreg `lhs' `rhs' `if', quantile(`quantile' ) reps(`reps')
}
else {
qreg
}
if "`grqreg'" != "" {
grqreg, ci
}
if "`save'" != "" {
disp ""
save "`save'", replace
}
end
------------------------------------------
> -----Original Message-----
> From: [email protected] [mailto:owner-
> [email protected]] On Behalf Of Kevin McCourt
> Sent: Monday, May 22, 2006 1:36 PM
> To: [email protected]
> Subject: st: CLAD error message and coefficient interpretation
>
> Hi everyone,
>
> I have two questions regarding STATA's CLAD procedure.
>
> Firstly, I have a censored dependent variable--it is
> fraction of plant capacity used. My data set is a
> panel of 10 years tracking about 30 different timber
> processing factories. Model specification requires
> fixed effects and I definitely have heteroskedasticity
> so tobit does not do what I need. I read Chay &
> Powell's paper and I think they might just provide the
> solution to my problem. When I input my data and run
> the model, I get an error that states one of my dummy
> variables that identifies one of the plants is
> missing. The variable IS there. Depending on the model
> specification (that is, what other explanatory
> variables I include), I get the same error message but
> for varying dummy variables. It is always the same
> message--"factoryZ not found" (where factoryZ is the
> name of the dummy). I have a suspicion this is caused
> by collinearity, because when I remove some of the
> variables the error is resolved. How can I know for
> sure what is going wrong? This error happens
> regardless of whether I use CLAD or CQREG (chay &
> powell's version).
>
> Also, how do I interpret the coefficients? Are they
> likelihood ratios or are they in units of my dependent
> variable? My dependent variable goes from 0 to 1.02. I
> ask this because one of the coefficients is 1.2 and I
> have a hard time understanding what it really implies.
> Also, should I use the BIAS value and substract it
> from the coefficient?
>
> thank you all in advance,
>
> Kevin McCourt
>
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