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From |
Melaku Fekadu <melaku.fekadu@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: duration analysis in gllamm |

Date |
Mon, 12 Jul 2010 17:25:21 +0300 |

Dear Statalisters, I want to estimate a duration model (time-to-first-employment) through gllamm with unobserved heterogeneity. An individual may experience transition in and out of states through years, as seen in below: from unemployment to employment, and from employment to unemployment. But, I am interested only about the first transition from unemployment (year 19XX=0) to employment (year 19XX=1). I wanted to ask two important questions about: 1. the data structure for gllamm estimation 2. the codes themselves in gllamm An example of the data is given below. Variables Year 1996 =1 if employed in 1996, 0 other wise, and so on X is some exogenous variable; it may be time-varying variable. In this example it is not so. Data – Table 1, (my data currently structured as follows), each individual has one row of observation with one entry for each year For the first individual (row vector) Id=1, year96=0, year97=0, year98=0, year99=0, year2000=1, x=12 For the second individual (row vector) Id=2, year96=1, year97=1, year98=1, year99=0, year2000=0, x=15 For the third individual (row vector) Id=3, year96=0, year97=0, year98=0, year99=0, year2000=0, x=10 For the fourth individual (row vector) Id=4, year96=0, year97=0, year98=0, year99=1, year2000=1, x=8 For the fifth individual (row vector) Id=5, year96=1, year97=1, year98=1, year99=1, year2000=1, x=17 Restructured data (Table 2) For the first individual – 5 rows of observation for each year Id=1, Event=0, x=12 Id=1, Event=0, x=12 Id=1, Event=0, x=12 Id=1, Event=1, x=12 Id=1, Event=1, x=12 For the second individual – 5 rows of observation for each year Id=2, Event=1, x=15 Id=2, Event=1, x=15 Id=2, Event=1, x=15 Id=2, Event=0, x=15 Id=2, Event=0, x=15 For the third individual – 5 rows of observation for each year Id=3, Event=0, x=10 Id=3, Event=0, x=10 Id=3, Event=0, x=10 Id=3, Event=0, x=10 Id=3, Event=0, x=10 For the fourth individual – 5 rows of observation for each year Id=4, Event=0, x=8 Id=4, Event=0, x=8 Id=4, Event=0, x=8 Id=4, Event=1, x=8 Id=4, Event=0, x=8 For the fifth individual – 5 rows of observation for each year Id=5, Event=1, x=17 Id=5, Event=1, x=17 Id=5, Event=1, x=17 Id=5, Event=1, x=17 Id=5, Event=1, x=17 Questions: 1. If I want to use stata's gllamm, should I convert my data from that of Table 1 to Table 2? 2. Should I discard observations collected after the first transition to employment has occurred? For example: In case of individual number one, observation 5 should be thrown? For individual number 2 (which is left censored, because he is already observed working in the first period), which observations should be thrown? Individual 3 is right-censored (has not yet experienced employment at all), so should all of his observations remain in the data? For individual 4, observation no 20 is collected after he has already experienced employment in the previous period, so should it be thrown? Individual 5 is left censored, so should his observations remain in the data or be thrown? 3. If the data is to be restructured, for estimation through gllamm, should the dependent variable be binary (one employed, 0 otherwise)? Or, should it be a variable that indicates how many years has passed until the individual became employed? For example: individual 1 is employed in the fourth year from the beginning of the observation, so the variable takes the value of 4; for individual 2, the variable takes value of 1 (since he is already employed in the first observation)? And so on. 4. How should look like a code in gllamm unobserved heterogeneity (parametric and non-parametric)? I will be grateful if you can indicate me on how to code this in gllamm. 5. I would be very grateful if you have some codes of gllamm which would give me some hints on how to code it. I really appreciate any help. Thanks a lot * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**Re: st: duration analysis in gllamm***From:*Maarten buis <maartenbuis@yahoo.co.uk>

**Re: st: duration analysis in gllamm***From:*Steve Samuels <sjsamuels@gmail.com>

**Re: st: duration analysis in gllamm***From:*Maarten buis <maartenbuis@yahoo.co.uk>

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