Hello everyone,
I am working on informality and poverty dynamics using panel data from Nigeria and I am trying to run an analysis using the "dynamic multinomial logit model" to estimate the informal employment dynamics. In this case, I am trying to ensure that the state dependence of the employment status i.e. the effect of past employment status on present employment status, is accounted for. However, using the "gllamm" command in stata (got this from the Stata forum), I am able to estimate the model but nothing about the state dependence shows in the result. I also tried the "gsem" command but no result on the state dependence. Hence, it appears I am not implementing it well.
Below is a snippet of my data:
Thank you
I am working on informality and poverty dynamics using panel data from Nigeria and I am trying to run an analysis using the "dynamic multinomial logit model" to estimate the informal employment dynamics. In this case, I am trying to ensure that the state dependence of the employment status i.e. the effect of past employment status on present employment status, is accounted for. However, using the "gllamm" command in stata (got this from the Stata forum), I am able to estimate the model but nothing about the state dependence shows in the result. I also tried the "gsem" command but no result on the state dependence. Hence, it appears I am not implementing it well.
Below is a snippet of my data:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input float(pid w_year w_inf_type) 1 2010 2 1 2012 4 1 2015 . 1 2018 . 8 2010 3 8 2012 3 8 2015 . 8 2018 . 15 2010 3 15 2012 2 15 2015 2 15 2018 . 21 2010 . 21 2012 . 21 2015 . 21 2018 . 25 2010 . 25 2015 3 25 2018 . 25 . . 34 2010 2 34 2012 2 34 2015 . 34 2018 . 41 2010 2 41 2012 1 41 2015 . 41 2018 . 45 2010 3 45 2012 2 45 2015 . 45 2018 . 137 2010 5 137 2012 6 137 2015 . 137 2018 . 142 2010 6 142 2012 1 142 2015 . 142 2018 . 148 2010 . 148 2012 . 148 2015 . 148 2018 6 149 2010 5 149 2012 . 149 2015 . 149 2018 . 161 2010 4 161 2012 . 161 2015 4 161 2018 . 167 2010 5 167 2012 . 167 2015 6 167 2018 . 173 2010 4 173 2012 . 173 2015 . 173 2018 . 178 2010 . 178 2012 5 178 2015 6 178 2018 . 191 2010 3 191 2012 2 191 2015 . 191 2018 . 242 2010 . 242 2012 . 242 2015 . 242 2018 . 249 2010 . 249 2012 . 249 2015 . 249 2018 . 252 2010 . 252 2012 5 252 2015 . 252 2018 . 257 2010 6 257 2012 6 257 2015 6 257 2018 . 261 2010 . 261 2012 . 261 2015 . 261 2018 . 264 2010 4 264 2012 . 264 2015 . 264 2018 . 266 2010 . 266 2012 . 266 2015 . 266 2018 . 272 2010 4 272 2012 . 272 2015 . 272 2018 2 end label values w_inf_type inff1 label def inff1 1 "Formal self employment", modify label def inff1 2 "Formal wage employment", modify label def inff1 3 "upper-tier informal wage", modify label def inff1 4 "Lower-tier Informal Wage", modify label def inff1 5 "Upper-tier Informal self", modify label def inff1 6 "Lower-tier Informal self", modify
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