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I have two groups (e.g. male/ female) and employment status (yes/no) per individual for 12 months in a row. Status may change every month (e.g. January employed, February unemployed, March employed, April employed....)

My intention is to find out whether both groups have a different pattern or not.

Any suggestions what approach might be suitable (eventually recommend a R package please).

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    $\begingroup$ There are specialised approaches to this but a simple way that I have found useful would be to model the time differences between the occurrences of a single binary response value (1 or 0). In other words, you don't model the occurrences of a Bernoulli event but the time taken between these occurrences (and hope that it doesn't come out as white noise). $\endgroup$
    – Digio
    Commented Jul 17, 2017 at 14:47
  • $\begingroup$ @Digio: Thanks for response. However I have problems with translating this approach in a mathematical system, because groups can switch their status every month from employment and unemployment and sample size is pretty large, so I need something automatized. Do you have an idea how I can deal with this issue (mathematical evaluation strategy)? $\endgroup$
    – Jens
    Commented Jul 20, 2017 at 13:52
  • $\begingroup$ You can achieve what I suggested with a simple SQL query that will transform your time series from binary to continuous within seconds before you start any kind of analysis. $\endgroup$
    – Digio
    Commented Jul 20, 2017 at 14:21
  • $\begingroup$ Since the goal is to understand the pattern of changing between groups, maybe some state model, start with a Markov model ... see for instance stats.stackexchange.com/questions/135573/… $\endgroup$ Commented Nov 2, 2021 at 17:06

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