Title
seqlogit10 postestimation -- Postestimation tools for seqlogit10
Description
post estimation tools specifically for seqlogit10:
seqlogitdecomp10 Makes a graph showing a decomposition of the effect of a variable on the highest achieved level of the dependent variable into effects of that variable on passing each transition and the importance of that transition as described in (Buis 2010).
uhdesc10 creates a table of describtive statistics of the unobserved variable at each transition. This is only available when the sd() option was used for seqlogit10.
seqlogit10_sensitivity is strictly speaking not a tool, but a helpfile showing how to run a sensitivity analysis with seqlogit10.
The following standard postestimation commands are also available:
command description ------------------------------------------------------------------------- INCLUDE help post_estat INCLUDE help post_estimates INCLUDE help post_lincom INCLUDE help post_lrtest INCLUDE help post_mfx INCLUDE help post_nlcom predict predictions INCLUDE help post_predictnl INCLUDE help post_suest INCLUDE help post_test INCLUDE help post_testnl -------------------------------------------------------------------------
------------------------------------------------------------------------------- help for seqlogitdecomp10 -------------------------------------------------------------------------------
Syntax for seqlogitdecomp10
seqlogitdecomp10 , overat(overatlist) [ at(atlist) levels(levellist) subtitle(titlelist) eqlabel(labellist) eqlegend xline(linearg) yline(linearg) title(title) name(name, replace) yscale(axis_suboptions) xscale(axis_suboptions) ysize(#) xsize(#) ]
Description
seqlogitdecomp10 displays a graph showing a decomposition of difference in the effect of a variable on the expected value of the dependent variable into effects of the variable on passing transitions and the importance if each transition (the weight) as described in (Buis 2010a). The variable whose effect will be decomposed is specified in the ofinterest() option in seqlogit10. For example, the dependent variable is the highest achieved level of education and the variable of interest is parental status, then seqlogitdecomp will decompose the effect of parental status on the highest achieved level of education into effects on passing each transition between levels of education and weights that represent the importance of that transition.
The effect on the expected value can differ between groups in the population. seqlogitdecomp10 shows how these differences are due to differences in effects on the transitions across groups and differences in the importance of each transition across groups. To continue the example: the effect of parental status can change over cohorts, and {seqlogitdecomp} will tell the extend to which this is due to changes in the effects on the transitions between levels of education or changes in the importance of each transition.
Options
+--------------------------------------+ ----+ Specifying the groups to be compared +-----------------------------
overat(overlist) Specifies the values of the explanatory variables of the groups that are to be compared. It overrides any value specified in the at option. Each comparison is seperated by a comma. The syntax for overlist is:
varname_1 # [varname_2 # [...]], varname_1 # [varname_2 # [...]], [...]
at(atlist) specifies the values at which the equations are evaluated. The syntax for atlist is: varname # [varname # ...]. The equations will be evaluated at the mean values of any of the variables not specified in at.
Say the dependent variable is highest achieved level of education, which is influenced by child's Socio Economic Status (ses) and cohort (coh) and the interaction between ses and coh (_ses_X_coh). We want to compare the decomposition of the effect of ses over different cohorts for mean value of ses. Say that coh has only three values: 1, 2, and 3 and the mean value of ses is .5. Than the overat and {otp at} options would read:
overat( coh 1, coh 2, coh 3 ) at( ses .5 )
Notice that the values for the interaction term need not be specified in the overat() option, as long as it was created using the over() option in seqlogit10.
+---------------+ ----+ Other options +----------------------------------------------------
levels(levellist) specifies the values attached to each level of the dependent variable. If it is not specified the values of the dependent variabel will be used. The syntax for levels is: # = # [, # = #, ...]
subtitel(titlelist) specifies the titles above each group, cohort in the example above. The syntax of titlelist is "string" "string" [...]. The number of titles must equal the number of groups.
eqlabel(labellist) specifies labels for each transition. The syntax of labellist is "string" "string" [...]. The number of labels must equal the number of transitions. If one wants to let the label span more than one line, one can use `" "string1" "string2" "'.
eqlegend specifies that a legend is used to identify the different transitions. By default the transitions are identified using titles on the right of the graph.
xline(numlist) see: added line options
yline(numlist) see: added line options
title(title) see: title_options
name(name, replace) see: name_option
[y|x]scale(axis sub options) see: axis_scale_options
[y|x]lable(rule_or_values) see: axis_options
[y|x]size(#) see: region_options
Example
sysuse nlsw88, clear gen ed = cond(grade< 12, 1, /// cond(grade==12, 2, /// cond(grade<16,3,4))) if grade < . gen byr = (1988-age-1950)/10 gen white = race == 1 if race < .
seqlogit10 ed byr south, /// ofinterest(white) over(byr) /// tree(1 : 2 3 4, 2 : 3 4, 3 : 4) /// levels(1=6, 2=12, 3=14, 4= 16) /// or
seqlogitdecomp10, /// overat(byr -.5, byr 0, byr .4) /// subtitle("1945" "1950" "1954") /// eqlabel(`""finish" "high school""' /// `""high school v" "some college""' /// `""some college v" "college""') /// xline(0) yline(0)
------------------------------------------------------------------------------- help for uhdesc10 -------------------------------------------------------------------------------
Syntax for uhdesc10
uhdesc10 , [ at(atlist) overat(overatlist) levels(levellist) overlab(stringlist) draws(#) ]
Description
uhdesc10 creates a table of describtive statistics of the unobserved variable at each transition. This is only available when the sd() option was used for seqlogit10. When the sd() option is specified one is estimating the parameters that would occur if there is an unobserved variable, which is normally distributed, wich at the first transition has a mean of zero, a standard deviation as specified in the sd() option, and is uncorrelated witht the observed covariates, and one correctly controlled for this unobserved variable. The consequences of such an unobserved variable and the way to estimate the parameters in such a scenario are discussed in (Buis 2011). The aim of uhdesc10 is to show what happens to this unobserved variable at the different transitions, and thus get an insight into why the estimates in the scenario are different (or not) from a regular sequential logit.
Options
overat(overlist) Specifies the values of the explanatory variables of the groups that are to be compared. It overrides any value specified in the at option. Each comparison is seperated by a comma. The syntax for overlist is:
varname_1 # [varname_2 # [...]], varname_1 # [varname_2 # [...]], [...]
at(atlist) specifies the values at which the equations are evaluated. The syntax for atlist is: varname # [varname # ...]. The equations will be evaluated at the mean values of any of the variables not specified in at.
Say the dependent variable is highest achieved level of education, which is influenced by child's Socio Economic Status (ses) and cohort (coh) and the interaction between ses and coh (_ses_X_coh). We want to compare the decomposition of the effect of ses over different cohorts for mean value of ses. Say that coh has only three values: 1, 2, and 3 and the mean value of ses is .5. Than the overat and {otp at} options would read:
overat( coh 1, coh 2, coh 3 ) at( ses .5 )
Notice that the values for the interaction term need not be specified in the overat() option, as long as it was created using the over() option in seqlogit10.
overlab(stringlist) specifies the label that is to be attached to each group specified in the overatlist() option. Spaces are not allowed but an _ will be displayed as an space. The number of labels has to be the same as the number of groups specified in the overatlist() option.
To continue the example above: Say that a value of 1 on the variable coh corresponds to the cohort born in 1950, a value 2 corresponds to the cohort born in 1970, the value 3 corresponds to the cohort born in 1990, then the {cmd overlab()} option would read:
overlab(1950 1970 1990)
levels(levellist) specifies the values attached to each level of the dependent variable. If it is not specified the values of the dependent variabel will be used. The syntax for levels is: # = # [, # = #, ...]
Example
sysuse nlsw88, clear gen ed = cond(grade< 12, 1, /// cond(grade==12, 2, /// cond(grade<16,3,4))) if grade < . gen byr = (1988-age-1950)/10 gen white = race == 1 if race < .
seqlogit10 ed byr south, /// ofinterest(white) over(byr) /// tree(1 : 2 3 4, 2 : 3 4, 3 : 4) /// or sd(1) uhdesc10
------------------------------------------------------------------------------- help for predict -------------------------------------------------------------------------------
Syntax for predict
predict [type] newvar [if] [in] [, statistic outcome(#) transition(#) choice(#) equation(#) levels(levellist) ]
statistic Description ------------------------------------------------------------------------- xb xb, fitted values stdp standard error of the prediction trpr probability of passing transition tratrisk proportion of respondents at risk of passing transition trvar variance of the indicator variable indicating whether or not the respondent passed the transition trgain difference in expected highest achieved level between those that pass the transition and those that do not trweight weight assigned to transition pr probability that an outcome is the highest achieved outcome. y expected highest achieved level effect Effect of variable of interest on expected highest achieved level. This variable is specified in the ofinterest() option in seqlogit. Interactions with the variables specified in the over() option of seqlogit are automatically taken into account. residuals difference between highest achieved level and expected highest achieved level. score first derivative of the log likelihood with respect to the linear predictor. -------------------------------------------------------------------------
Options for predict
transition(#) specifies the transition, 1 is the first transition specified in the tree option in seqlogit, 2 the second, etc.
choice(#) specifies the choice within the transition, 0 is the choice (the reference category), 1 the second, etc.
equation(#) specifies the equation, #1 is the first equation, #2 the second, etc.
levels(levellist) specifies the values attached to each level of the dependent variable. If it is not specified the values of the dependent variabel will be used. The syntax for levels is: # = # [, # = #, ...]
References
Buis, Maarten L. 2010 ``Chapter 6, Not all transitions are equal: The relationship between inequality of educational opportunities and inequality of educational outcomes'', In: Buis, Maarten L. ``Inequality of Educational Outcome and Inequality of Educational Opportunity in the Netherlands during the 20th Century''. PhD thesis. http://www.maartenbuis.nl/dissertation/chap_6.pdf
Buis, maarten L. 2011 ``The Consequences of Unobserved Heterogeneity in a Sequential Logit Model'', Research in Social Stratification and Mobility, 29(3), pp. 247-262. http://dx.doi.org/10.1016/j.rssm.2010.12.006
Also see
Online: help for seqlogit, estimates, lincom, lrtest, mfx, nlcom, predictnl, suest, test, testnl