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From |
"Julia Korosteleva" <j.korosteleva@ucl.ac.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
st: multinomial multilevel random intercept model using gllamm |

Date |
Mon, 22 Aug 2011 15:32:42 +0100 |

Greetings, I am terribly sorry for disturbing you but I would appreciate your help with my model. I am currently running a 3-level random intercept multinomial model with respondents denoting level 1; years - level 2; and countries - level 3. I?ve first deleted all cases with missing values. I then collapsed the dataset and created level 1 weights to speed up the estimations as per advice provided in the gllamm manual. For this used all the varaibles that are appear in the regression. Is it correct? collapse (count) wt1=cons, by(year country l3r_hfgov l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp SUYR5JOBml2 age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fearfail_dum) Next I created a new variable - alternatives correspondent to each of the 3 options of the dependent variable. sort year country SUYR5JOBml2 here year represents level 2, country ? level 3 and SUYR5JOBml2 is my dependent variable which represents start-up growth expectations and it contains 3 responses: (1) don?t enter entrepreneurship; (2) enter self-employment; (3) enter entrepreneurship with intentions to generate employment (more than 1 job in 5 years time). I further created an ID variable for the dataset. gen id=_n expand 3 sort id qui by id: gen alt=_n gen chosen=alt== SUYR5JOBml2 tab alt, gen(a) eq a2: a2 eq a3: a3 However at this stage I have a major concern. I am not entirely sure whether I do everything correctly in expanding the data by replacing each record with 3 replicates and generating a new variable alt. If the total No of obs after deletion of cases with missing values is about 450,000 I don?t really understand why a number of observations for the new variable alt expands to 586,818 and why the final No of obs goes up to 1,430,000. Could you please explain in more detail how this ?expanded? option works and advise on whether I expand the data correctly based on the syntax above. . tab alt, gen(a) alt | Freq. Percent Cum. ------------+----------------------------------- 1 | 195,606 33.33 33.33 2 | 195,606 33.33 66.67 3 | 195,606 33.33 100.00 ------------+----------------------------------- Total | 586,818 100.00 Below is the model itself. I have a number of level 1 characteristics of start-ups to predict the odds of choosing a specific alternative (out of the 3 mentioned earlier). Further I have level 2 variables which are lagged values of the institutional indicators (e.g. l3rhfgov stands for the 3?rd lag of the size of the state and so on). . gllamm alt age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fearfail_dum l3r_hfgov l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp, expand(id chosen m) i(year country) link(mlogit) family(binom) eqs(a2 a3) nip(4) weight(wt) adapt trace dots When I add the 'nrf' option I can onyl specify (1, 1) which I believe is not exactly correct as I need to random effects to be displayed for each level so to have (2, 2). However, I get a warning message when I try to do this. Furthermore, I wonder whether I also need to add an overall constant so to have 'eq const' and then to specify nrf(2, 2, 2). So, so far I've taken out nrf option form the model allowing it to be a default one (1,1) which would show ransom intercept one of the response only. Below are seom preliminary information on the estimation of the model for more details. gllamm alt age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fearfail_dum l3r_hfgo > v l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp, expand(id chosen m) i(year country) link(mlogit) family(binom) eqs(a > 2 a3) nip(4) weight(wt) adapt trace dots General model information ------------------------------------------------------------------------------ dependent variable: alt nominal responses: mlogit denominator: 1 equations for fixed effects c2: age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fe > arfail_dum l3r_hfgov l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp _cons c3: age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fe > arfail_dum l3r_hfgov l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp _cons Random effects information for 3 level model ------------------------------------------------------------------------------ ***level 2 (year) equation(s): (1 random effect(s)) standard deviation for random effect 1 yea1_1 : a2 ***level 3 (country) equation(s): (1 random effect(s)) standard deviation for random effect 2 cou2_1 : a3 number of level 1 units = 1430103 number of level 2 units = 187 number of level 3 units = 51 Initial values for fixed effects (using gllamm for inital values) In the zero iteration all coefficients were '0'. Now I am at iteration 5 after 12 hrs of the model running. Please advise if I am doing everything correctly. The last thing. I've also tried to estimate the model without expanding the dataset and leaving the origianl dependent variable denoting 3 alternatives. gllamm SUYR5JOBml2 age age_sq male gemwork educ_secpost educ_post busang_dum omestb_dum knowent_dum fearfail_dum l3r_hfgov l3_exconst l3_finfree l_dgdp l_gdp_pc_ppp, i(year country) link(mlogit) family(binom) basecategory(1) nip(4) adapt trace dots This one is stuck at iteration 1 without no further progress after 14 hrs of running. Odd enough, when I started running this specification a few days ago and after 1 day I discovered that there was not much progress after iteration 0 I stopped running it. Once I broke it I got the results of iteration 1 appearing on the screen and the following message followed: "conformability error finish running on 22 Aug 2011 at 03:12:22". Do you think it is worth giving another go? I've re-launched it again but as I mentioned earlier after 14 hrs I still get onyl iteration 0, and for some reasons I cannot see dots to judge whether the programme crashed or not. Please also advise which of the two specifications would be preferable. Many thanks, Julia -- Dr Julia Korosteleva Lecturer in Business Economics Affiliate Tutor School of Slavonic and East European Studies University College London 16 Taviton Street London, WC1H 0BW Tel.: 020 76797590 Fax: 020 7679 8755 E-mail: J.Korosteleva@ucl.ac.uk * * 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/

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