Hello,

I have a panel data set from 2013 - 2017 where each individual is included for 3 years.
My dependent variable is called "membership", which describes if an individual is a union member or not in a given year. I would like to estimate the probability of being a member for the following years (2014-2017), using 2013 as my base year, so that I am allowed to compare different probabilities from different years. Besides that I would like to include the following categorical variables: gender (2 categories), age (7 categories), occupation (9 categories).
I have tried this so far:

fvset base 1 SEX
fvset base 2013 YEAR
fvset base 1 AGE
fvset base 1 OCCU

regress MEMBER YEAR#SEX YEAR#AGE YEAR#OCCU /*for simplicity I use regress...eventually this should become a logit */


note: 2014.YEAR#7.AGE omitted because of collinearity
note: 2015.YEAR#7.AGE omitted because of collinearity
note: 2016.YEAR#2.AGE identifies no observations in the sample
note: 2016.YEAR#7.AGE omitted because of collinearity
note: 2017.YEAR#2.AGE identifies no observations in the sample
note: 2017.YEAR#7.AGE omitted because of collinearity
note: 2013b.YEAR#0.OCCU identifies no observations in the sample
note: 2014.YEAR#0.OCCU identifies no observations in the sample
note: 2014.YEAR#7.OCCU omitted because of collinearity
note: 2015.YEAR#7.OCCU omitted because of collinearity
note: 2016.YEAR#0.OCCU identifies no observations in the sample
note: 2016.YEAR#4.OCCU omitted because of collinearity
note: 2016.YEAR#5.OCCU identifies no observations in the sample
note: 2016.YEAR#7.OCCU omitted because of collinearity
note: 2017.YEAR#0.OCCU identifies no observations in the sample
note: 2017.YEAR#4.OCCU omitted because of collinearity
note: 2017.YEAR#5.OCCU identifies no observations in the sample
note: 2017.YEAR#7.OCCU omitted because of collinearity

Source | SS df MS Number of obs = 7,415
-------------+---------------------------------- F(64, 7350) = 160.61
Model | 358.224026 64 5.59725041 Prob > F = 0.0000
Residual | 256.147383 7,350 .034849984 R-squared = 0.5831
-------------+---------------------------------- Adj R-squared = 0.5794
Total | 614.371409 7,414 .082866389 Root MSE = .18668

------------------------------------------------------------------------------
MEmber| Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
YEAR#SEX |
2013 2 | .004884 .0087605 0.56 0.577 -.0122891 .022057
2014 1 | -.0776703 .0351981 -2.21 0.027 -.1466688 -.0086719
2014 2 | -.067912 .0353275 -1.92 0.055 -.1371641 .0013401
2015 1 | -.0933623 .0350606 -2.66 0.008 -.162091 -.0246335
2015 2 | -.0993364 .0351908 -2.82 0.005 -.1683205 -.0303523
2016 1 | -.9903619 .0444191 -22.30 0.000 -1.077436 -.9032876
2016 2 | -.9903619 .0471997 -20.98 0.000 -1.082887 -.8978369
2017 1 | -.9903619 .0426139 -23.24 0.000 -1.073897 -.9068264
2017 2 | -.9903619 .0451187 -21.95 0.000 -1.078807 -.9019163
|
YEAR#AGE|
2013 2 | .006359 .0248065 0.26 0.798 -.0422688 .0549868
2013 3 | .0042794 .0332617 0.13 0.898 -.060923 .0694818
2013 4 | -.0028027 .0338723 -0.08 0.934 -.0692021 .0635968
2013 5 | -.0251489 .0343106 -0.73 0.464 -.0924076 .0421098
2013 6 | -.0182281 .0361568 -0.50 0.614 -.0891059 .0526496
2013 7 | -.0134905 .0378788 -0.36 0.722 -.0877438 .0607628
2014 1 | .0554668 .0326542 1.70 0.089 -.0085448 .1194784
2014 2 | .055943 .0296398 1.89 0.059 -.0021595 .1140454
2014 3 | .0531463 .0222732 2.39 0.017 .0094844 .0968081
2014 4 | .0586396 .0194622 3.01 0.003 .0204881 .0967911
2014 5 | .0178848 .0192012 0.93 0.352 -.0197551 .0555247
2014 6 | .0221597 .0144085 1.54 0.124 -.0060851 .0504045
2014 7 | 0 (omitted)
2015 1 | .112982 .0354066 3.19 0.001 .0435749 .1823891
2015 2 | .1141303 .0300446 3.80 0.000 .0552342 .1730263
2015 3 | .1130865 .0210257 5.38 0.000 .0718701 .1543028
2015 4 | .0911164 .0183987 4.95 0.000 .0550498 .1271831
2015 5 | .0581264 .0177381 3.28 0.001 .0233546 .0928982
2015 6 | .0491886 .013874 3.55 0.000 .0219915 .0763857
2015 7 | 0 (omitted)
2016 1 | 1.70e-15 .1889592 0.00 1.000 -.3704142 .3704142
2016 2 | 0 (empty)
2016 3 | 2.59e-15 .1201691 0.00 1.000 -.2355658 .2355658
2016 4 | 2.38e-15 .067165 0.00 1.000 -.1316627 .1316627
2016 5 | 3.38e-15 .0554618 0.00 1.000 -.108721 .108721
2016 6 | 3.56e-15 .0401181 0.00 1.000 -.078643 .078643
2016 7 | 0 (omitted)
2017 1 | 7.69e-15 .1105848 0.00 1.000 -.2167778 .2167778
2017 2 | 0 (empty)
2017 3 | 6.37e-15 .0930849 0.00 1.000 -.182473 .182473
2017 4 | 6.27e-15 .056955 0.00 1.000 -.1116482 .1116482
2017 5 | 7.24e-15 .0445423 0.00 1.000 -.0873156 .0873156
2017 6 | 7.89e-15 .0346907 0.00 1.000 -.0680037 .0680037
2017 7 | 0 (omitted)
|

My thought behind that is to set 2013 as my base year and then interact it with the other independent variables. Nevertheless, I do not know how to include the information about being a member in 2013 (or any other previous years), since membership is my dependent variable.
I would really appreciate any help. I have been reading a lot online,but nothing seems to help me being less confused.

Thank You

Hannah