Dear Statalist

This is a question about interpreting the results from a panel data fixed-effects logistic regression. The outcome variable is binary & the main regressor is categorical with 4 levels.

As the estimated odds ratios change depending on which base level is selected, in a cross-sectional setting I prefer to use -margins- and interpret the results in terms of average adjusted predictions (which is unaffected by the base level). However, when using -xtlogit-, the average adjusted predictions appear to change depending on the base level.

If we treat the data as cross-sectional, the results from -margins- are unchanged by the base level of the regressor.

This is not the case, however, with panel-data -xtlogit-

Thanks,

Junran

This is a question about interpreting the results from a panel data fixed-effects logistic regression. The outcome variable is binary & the main regressor is categorical with 4 levels.

As the estimated odds ratios change depending on which base level is selected, in a cross-sectional setting I prefer to use -margins- and interpret the results in terms of average adjusted predictions (which is unaffected by the base level). However, when using -xtlogit-, the average adjusted predictions appear to change depending on the base level.

**Question**: is this the expected behaviour for -margins- after -xtlogit-? If so, would it be preferable to interpret the results in terms of odds ratio instead of probabilities in a panel-data setting?Code:

use http://www.stata-press.com/data/r16/union.dta, clear xtset idcode year, yearly * Discretize the -grade- variable into 4 levels for illustration purpose egen grade_category = cut(grade), at(0,7,13,16,19) icodes label define grade_category 0 "primary" 1 "secondary" 2 "undergraduate" 3 "postgraduate" label values grade_category grade_category

Code:

quietly logit union i.year ib(0).grade_category margins grade_category Predictive margins Number of obs = 26,200 Model VCE : OIM Expression : Pr(union), predict() -------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- grade_category | primary | .2349991 .0276247 8.51 0.000 .1808556 .2891425 secondary | .2073589 .0031732 65.35 0.000 .2011395 .2135782 undergraduate | .1943004 .0058311 33.32 0.000 .1828718 .2057291 postgraduate | .2937748 .0064781 45.35 0.000 .281078 .3064717 -------------------------------------------------------------------------------- quietly logit union i.year ib(1).grade_category margins grade_category *(output omitted) quietly logit union i.year ib(2).grade_category margins grade_category *(output omitted) quietly logit union i.year ib(3).grade_category margins grade_category *(output omitted)

Code:

. quietly xtlogit union i.year ib(0).grade_category, fe . margins grade_category Predictive margins Number of obs = 12,035 Model VCE : OIM Expression : Pr(union|fixed effect is 0), predict(pu0) -------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- grade_category | primary | .5184114 .0215869 24.02 0.000 .4761018 .5607209 secondary | .5703154 .2774507 2.06 0.040 .026522 1.114109 undergraduate | .5507514 .2823345 1.95 0.051 -.0026142 1.104117 postgraduate | .6687735 .2569906 2.60 0.009 .1650813 1.172466 -------------------------------------------------------------------------------- . quietly xtlogit union i.year ib(1).grade_category, fe . margins grade_category Predictive margins Number of obs = 12,035 Model VCE : OIM Expression : Pr(union|fixed effect is 0), predict(pu0) -------------------------------------------------------------------------------- | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] ---------------+---------------------------------------------------------------- grade_category | primary | .4661257 .2823028 1.65 0.099 -.0871777 1.019429 secondary | .5184114 .0215869 24.02 0.000 .4761018 .5607209 undergraduate | .4985708 .0396701 12.57 0.000 .4208188 .5763228 postgraduate | .6207837 .0584854 10.61 0.000 .5061544 .735413 -------------------------------------------------------------------------------- *and so on

Junran

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