Choice Modelling
. use restaurant, clear
. nlogitgen type = restaurant(fast:Freebirds | PotatoShack, ///
family: WingsNmore | LosNortenos | Amas, ///
fancy: Christophers | CafeEccell)
new variable type is generated with 3 groups
label list lb_type
lb_type:
1 fast
2 family
3 fancy
. nlogit chosen cost distance rating || type: income kids, ///
base(family) || restaurant:, noconst case(family_id)
tree structure specified for the nested logit model
type N restaurant N k
-------------------------------------
fast 600 --- Freebirds 300 12
+- PotatoShack 300 15
family 900 --- Amas 300 78
|- LosNortenos 300 75
+- WingsNmore 300 69
fancy 600 --- Christophers 300 27
+- CafeEccell 300 24
-------------------------------------
total 2100 300
k = number of times alternative is chosen
N = number of observations at each level
Iteration 0: log likelihood = -541.93581
Iteration 1: log likelihood = -517.95909 (backed up)
Iteration 2: log likelihood = -511.99261 (backed up)
(output omitted)
Iteration 16: log likelihood = -485.47333
Iteration 17: log likelihood = -485.47331
RUM-consistent nested logit regression Number of obs = 2100
Case variable: family_id Number of cases = 300
Alternative variable: restaurant Alts per case: min = 7
avg = 7.0
max = 7
Wald chi2(7) = 46.71
Log likelihood = -485.47331 Prob > chi2 = 0.0000
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chosen | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
restaurant |
cost | -.1843847 .0933975 -1.97 0.048 -.3674404 -.0013289
distance | -.3797474 .1003828 -3.78 0.000 -.5764941 -.1830007
rating | .463694 .3264935 1.42 0.156 -.1762215 1.10361
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type equations
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fast |
income | -.0266038 .0117306 -2.27 0.023 -.0495952 -.0036123
kids | -.0872584 .1385026 -0.63 0.529 -.3587184 .1842016
-------------+----------------------------------------------------------------
family |
income | (base)
kids | (base)
-------------+----------------------------------------------------------------
fancy |
income | .0461827 .0090936 5.08 0.000 .0283595 .0640059
kids | -.3959413 .1220356 -3.24 0.001 -.6351267 -.1567559
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dissimilarity parameters
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type |
/fast_tau | 1.712878 1.48685 -1.201295 4.627051
/family_tau | 2.505113 .9646351 .614463 4.395763
/fancy_tau | 4.099844 2.810123 -1.407896 9.607583
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LR test for IIA (tau = 1): chi2(3) = 6.87 Prob > chi2 = 0.0762
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