Hi Statalist. I would like to be able to identify life events within a panel dataset with multiple waves. A large number of questions are asked about the life and experiences of participants within households on an annual basis, including if they 'married', 'have a baby', 'separate from spouse' or if they experience a 'worsening in finances', such as bankruptcy and so on (coded lemarr, lebth, lesep and lefnw). These variables are based on a [1] No, [2] Yes response.

I would like to be able to search through the waves of data (which I've already merged into a single file) to identify changes in an individual's self-rated life satisfaction over time based on the effect of certain life events or experiences throughout the panel data. While many of these related questions are asked annually, if the respondent experiences a given life event, they are asked for further information, such as the quarter in which the life event occurred so it would be good to factor this in if possible. Finally, I have data on both the respondent and their partner and this is represented in the naming of variables, such as lemarr (for the respondent) and p_lemarr (for the partner).

I have the following code which someone else helped me with for a different piece of work, but not sure if it is good code or if it is suitable for the task, so I appreciate any suggestions or guidance with this:

Code:
sort xwaveid wave
cap drop droppout 
gen droppout = mi(mrcurr) 

cap drop change_to_* 
gen change_to_married = 0 if droppout == 0 
bys id (wave): replace change_to_married = 1 if marstat == 1 & marstat[_n-1] != 1 & marstat[_n-1] != . & marstat != . // marstat - marital status  
gen change_to_sep = 0 if droppout == 0
bys id (wave): replace change_to_sep = 1 if marstat == 3 & marstat[_n-1] == 1 & marstat[_n-1] != . & marstat != .
bys id (wave): egen change_to_married_N = sum(change_to_married) if droppout == 0
bys id (wave): egen change_to_sep_N = sum(change_to_sep) if droppout == 0
cap drop nwave 
gen nwave = -wave 
gen timeline_married = .     
replace timeline_married = 0 if change_to_married == 1
bys id (wave): replace timeline_married = timeline_married[_n-1] + 1 if timeline_married == . 
bys id (nwave): replace timeline_married = timeline_married[_n-1] - 1 if timeline_married == . 
order timeline_married, after(change_to_married) 
gen timeline_sep = . 
replace timeline_sep = 0 if change_to_sep == 1     
bys id (wave): replace timeline_sep = timeline_sep_div[_n-1] + 1 if timeline_sep == .  
bys id (nwave): replace timeline_sep = timeline_sep[_n-1] - 1 if timeline_sep == . 
order timeline_sep, after(change_to_sep)        
gen time_single_married = timeline_single_married + 100
gen time_defacto_married = timeline_defacto_married + 100
A sample of the data follows (note that this applies to the respondent only, but could be duplicated to imitate the partner data if needed):
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(lemar lebth lesep lefnw)
1 1 1 1
2 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 1 1 1
1 2 1 1
1 2 1 1
2 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 1 1 1
1 2 1 1
1 2 1 1
1 1 1 1
2 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 2 1 1
1 1 1 1
1 1 1 2
1 1 1 1
2 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 1 1 1
1 1 1 1
1 1 1 1
1 2 1 1
1 2 1 1
1 1 1 1
1 1 1 1
1 1 1 2
1 1 1 1
end
label values lemar RLEME
label values lesep RLEME
label values lebth RLEME
label values lefnw RLEME
label def RLEME 1 "[1] No", modify
label def RLEME 2 "[2] Yes", modify
Please let me know if you need more information.

I appreciate any support/guidance. Kind regards, Chris