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Re: st: cascading dummies
From
Maarten Buis <[email protected]>
To
[email protected]
Subject
Re: st: cascading dummies
Date
Tue, 2 Oct 2012 09:59:17 +0200
On Tue, Oct 2, 2012 at 1:18 AM, Shikha Sinha wrote:
> You and also STB mentioned that coeff in regression using ordinal
> dummies can be constructed by subtracting the coeff from the
> regression using the standard dummies. Is this true for non-linear
> regression as well (Logit, probit)?
<snip>
> Does the odds ratios of 1.397527 mean that an increase in the dummy
> from 3 to 4 results in 39% increase in literacy?
I would do this type of analysis with -contrasts- command. Below is an
example that deals with the comparison of -cascade- and -constrast-,
the interpretation, and its use in a -logit- model.
*--------------------- begin example ----------------------
//========================== prepare data
sysuse nlsw88, clear
gen byte ed = cond(grade < 12, 1, ///
cond(grade == 12, 2, ///
cond(grade < 16, 3, ///
cond(grade < . , 4, .))))
label define ed 1 "less than high school" ///
2 "high school" ///
3 "some college" ///
4 "college"
label value ed ed
// =============== interpret "normal" coefficients
// estimate model
reg wage i.ed
// This gives the mean wages for each category
margins ed
// The constant is the mean wage of less than high school:
di _b[_cons]
// The coefficient of 2.ed is the difference between
// less than high school and high school, that is
// the constant + 2.ed = the mean wage of 2.ed:
di _b[_cons] + _b[2.ed]
//=============== -cascade- is superceded by -constrast-
// -cascade-
cascade ed if ed < ., gen(foo)
reg wage foo*
// -contrast-
reg wage i.ed
contrast ar.ed
// =================== interpretation of these contasts
// get the mean wages within each eductional category
margins ed, post
// first contrast is difference in mean wage between
// less than highschool and highschool:
di _b[2.ed] - _b[1.ed]
// second contrast is difference in mean wage between
// highschool and some college:
di _b[3.ed] - _b[2.ed]
// third contrast is difference in mean wage between
// some college and college:
di _b[4.ed] - _b[3.ed]
// I do these computations to show that we do not need
// to do them, the results are already given in the
// output of -contrast-
// ====================== non-linear model
gen byte good_job = occupation < 3 if occupation < .
logit good_job i.ed, or
// get the contrasts
contrast ar.ed, eform
// get the odds
margins i.ed, expression(exp(xb())) post
// first contrast is the ratio of the odds for less
// than high school and highschool
di _b[2.ed]/_b[1.ed]
// second contrast is the ratio of the odds for
// high school and some college
di _b[3.ed]/_b[2.ed]
// third contrast is the ratio of the odds for
// some college and college
di _b[4.ed]/_b[3.ed]
*---------------------- 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
WZB
Reichpietschufer 50
10785 Berlin
Germany
http://www.maartenbuis.nl
---------------------------------
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