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log: c:\Imbook\bwebpage\Section4\mma15p2gev.txt
log type: text
opened on: 19 May 2005, 12:16:29
.
. ********** OVERVIEW OF MMA15P2GEV.DO **********
.
. * STATA Program
. * copyright C 2005 by A. Colin Cameron and Pravin K. Trivedi
. * used for "Microeconometrics: Methods and Applications"
. * by A. Colin Cameron and Pravin K. Trivedi (2005)
. * Cambridge University Press
.
. * Chapter 15.6.3 page 511
. * Nested logit (GEV) model analysis.
. * (1) Set data up and reproduce Mixed estimates in Table 15.2 p.493
. * (2A) Nested logit model estimates (page 511)
. * (2B) Restricted nested logit model estimates (page 511)
. * (2C) Equivalent conditional logit model estimates (same as (2B))
.
. * Related programs are
. * mma15p1mnl.do multinomial and conditional logit using Stata
. * mma15p3mnl.lim multinomial logit using Limdep
. * mma15p4gev.lim conditional and nested logit using Limdep and Nlogit
.
. * To run this program you need data file
. * Nldata.asc
.
. * NOTE: The example here is deliberately simple and merely illustrative.
. * with nesting structure
. * / \
. * / \ / \
. * In this case with parameter rho_j differing across alternatives
. * Stata 8 estimates the earlier variant of the nested logit model
. * rather than the preferred variant given in the text.
. * See the discussion at bottom of page 511 and also Train (2003, p.88)
.
. ********** SETUP **********
.
. set more off
. version 8.0
. set scheme s1mono /* Graphics scheme */
.
. ********** DATA DESCRIPTION **********
.
. * Data Set comes from :
. * J. A. Herriges and C. L. Kling,
. * "Nonlinear Income Effects in Random Utility Models",
. * Review of Economics and Statistics, 81(1999): 62-72
.
. * The data are given as a combined observation with data on all 4 choices.
. * This will work for multinomial logit program.
. * For conditional logit will need to make a new data set which has
. * four separate entries for each observation as there are four alternatives.
.
. * Filename: NLDATA.ASC
. * Format: Ascii
. * Number of Observations: 1182
. * Each observations appears over 3 lines with 4 variables per line
. * so 4 x 1182 = 4728 observations
. * Variable Number and Description
. * 1 Recreation mode choice. = 1 if beach, = 2 if pier; = 3 if private boat; = 4 if charter
. * 2 Price for chosen alternative
. * 3 Catch rate for chosen alternative
. * 4 = 1 if beach mode chosen; = 0 otherwise
. * 5 = 1 if pier mode chosen; = 0 otherwise
. * 6 = 1 if private boat mode chosen; = 0 otherwise
. * 7 = 1 if charter boat mode chosen; = 0 otherwise
. * 8 = price for beach mode
. * 9 = price for pier mode
. * 10 = price for private boat mode
. * 11 = price for charter boat mode
. * 12 = catch rate for beach mode
. * 13 = catch rate for pier mode
. * 14 = catch rate for private boat mode
. * 15 = catch rate for charter boat mode
. * 16 = monthly income
.
. ******* (1) CONDITIONAL LOGIT MODEL (Table 15.2 p.493 Mixed column) *********
.
. infile mode price crate dbeach dpier dprivate dcharter pbeach ppier /*
> */ pprivate pcharter qbeach qpier qprivate qcharter income /*
> */ using nldata.asc
(1182 observations read)
.
. gen ydiv1000 = income/1000
.
. * Data are one entry per individual
. * Need to reshape to 4 observations per individual - one for each alternative
. * Use reshape to do this which also creates variable (see below)
. * alternatv = 1 if beach, = 2 if pier; = 3 if private boat; = 4 if charter
. gen id = _n
. gen d1 = dbeach
. gen p1 = pbeach
. gen q1 = qbeach
. gen d2 = dpier
. gen p2 = ppier
. gen q2 = qpier
. gen d3 = dprivate
. gen p3 = pprivate
. gen q3 = qprivate
. gen d4 = dcharter
. gen p4 = pcharter
. gen q4 = qcharter
. summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
mode | 1182 3.005076 .9936162 1 4
price | 1182 52.08197 53.82997 1.29 666.11
crate | 1182 .3893684 .5605964 .0002 2.3101
dbeach | 1182 .1133672 .3171753 0 1
dpier | 1182 .1505922 .3578023 0 1
-------------+--------------------------------------------------------
dprivate | 1182 .3536379 .4783008 0 1
dcharter | 1182 .3824027 .4861799 0 1
pbeach | 1182 103.422 103.641 1.29 843.186
ppier | 1182 103.422 103.641 1.29 843.186
pprivate | 1182 55.25657 62.71344 2.29 666.11
-------------+--------------------------------------------------------
pcharter | 1182 84.37924 63.54465 27.29 691.11
qbeach | 1182 .2410113 .1907524 .0678 .5333
qpier | 1182 .1622237 .1603898 .0014 .4522
qprivate | 1182 .1712146 .2097885 .0002 .7369
qcharter | 1182 .6293679 .7061142 .0021 2.3101
-------------+--------------------------------------------------------
income | 1182 4099.337 2461.964 416.6667 12500
ydiv1000 | 1182 4.099337 2.461964 .4166667 12.5
id | 1182 591.5 341.3583 1 1182
d1 | 1182 .1133672 .3171753 0 1
p1 | 1182 103.422 103.641 1.29 843.186
-------------+--------------------------------------------------------
q1 | 1182 .2410113 .1907524 .0678 .5333
d2 | 1182 .1505922 .3578023 0 1
p2 | 1182 103.422 103.641 1.29 843.186
q2 | 1182 .1622237 .1603898 .0014 .4522
d3 | 1182 .3536379 .4783008 0 1
-------------+--------------------------------------------------------
p3 | 1182 55.25657 62.71344 2.29 666.11
q3 | 1182 .1712146 .2097885 .0002 .7369
d4 | 1182 .3824027 .4861799 0 1
p4 | 1182 84.37924 63.54465 27.29 691.11
q4 | 1182 .6293679 .7061142 .0021 2.3101
.
. reshape long d p q, i(id) j(alterntv)
(note: j = 1 2 3 4)
Data wide -> long
-----------------------------------------------------------------------------
Number of obs. 1182 -> 4728
Number of variables 30 -> 22
j variable (4 values) -> alterntv
xij variables:
d1 d2 ... d4 -> d
p1 p2 ... p4 -> p
q1 q2 ... q4 -> q
-----------------------------------------------------------------------------
. * This automatically creates alterntv = 1 (beach), ... 4 (charter)
. describe
Contains data
obs: 4,728
vars: 22
size: 420,792 (95.9% of memory free)
-------------------------------------------------------------------------------
storage display value
variable name type format label variable label
-------------------------------------------------------------------------------
id float %9.0g
alterntv byte %9.0g
mode float %9.0g
price float %9.0g
crate float %9.0g
dbeach float %9.0g
dpier float %9.0g
dprivate float %9.0g
dcharter float %9.0g
pbeach float %9.0g
ppier float %9.0g
pprivate float %9.0g
pcharter float %9.0g
qbeach float %9.0g
qpier float %9.0g
qprivate float %9.0g
qcharter float %9.0g
income float %9.0g
ydiv1000 float %9.0g
d float %9.0g
p float %9.0g
q float %9.0g
-------------------------------------------------------------------------------
Sorted by: id alterntv
Note: dataset has changed since last saved
. summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
id | 4728 591.5 341.25 1 1182
alterntv | 4728 2.5 1.118152 1 4
mode | 4728 3.005076 .9933008 1 4
price | 4728 52.08197 53.81289 1.29 666.11
crate | 4728 .3893684 .5604185 .0002 2.3101
-------------+--------------------------------------------------------
dbeach | 4728 .1133672 .3170746 0 1
dpier | 4728 .1505922 .3576888 0 1
dprivate | 4728 .3536379 .478149 0 1
dcharter | 4728 .3824027 .4860256 0 1
pbeach | 4728 103.422 103.6081 1.29 843.186
-------------+--------------------------------------------------------
ppier | 4728 103.422 103.6081 1.29 843.186
pprivate | 4728 55.25657 62.69354 2.29 666.11
pcharter | 4728 84.37924 63.52448 27.29 691.11
qbeach | 4728 .2410113 .1906919 .0678 .5333
qpier | 4728 .1622237 .1603389 .0014 .4522
-------------+--------------------------------------------------------
qprivate | 4728 .1712146 .2097219 .0002 .7369
qcharter | 4728 .6293679 .7058901 .0021 2.3101
income | 4728 4099.337 2461.183 416.6667 12500
ydiv1000 | 4728 4.099337 2.461183 .4166667 12.5
d | 4728 .25 .4330585 0 1
-------------+--------------------------------------------------------
p | 4728 86.61996 88.01813 1.29 843.186
q | 4728 .3009544 .4335593 .0002 2.3101
.
. * Bring in alternative specific dummies
. * Since d2-d4 already used instead call them dummy2 - dummy4
. gen obsnum=_n
. gen dummy1 = (mod(obsnum,4)==1) * 1
. gen dummy2 = (mod(obsnum,4)==2) * 1
. gen dummy3 = (mod(obsnum,4)==3) * 1
. gen dummy4 = (mod(obsnum,4)==0) * 1
. gen d1y = (mod(obsnum,4)==1) * ydiv1000
. gen d2y = (mod(obsnum,4)==2) * ydiv1000
. gen d3y = (mod(obsnum,4)==3) * ydiv1000
. gen d4y = (mod(obsnum,4)==0) * ydiv1000
.
. summarize
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
id | 4728 591.5 341.25 1 1182
alterntv | 4728 2.5 1.118152 1 4
mode | 4728 3.005076 .9933008 1 4
price | 4728 52.08197 53.81289 1.29 666.11
crate | 4728 .3893684 .5604185 .0002 2.3101
-------------+--------------------------------------------------------
dbeach | 4728 .1133672 .3170746 0 1
dpier | 4728 .1505922 .3576888 0 1
dprivate | 4728 .3536379 .478149 0 1
dcharter | 4728 .3824027 .4860256 0 1
pbeach | 4728 103.422 103.6081 1.29 843.186
-------------+--------------------------------------------------------
ppier | 4728 103.422 103.6081 1.29 843.186
pprivate | 4728 55.25657 62.69354 2.29 666.11
pcharter | 4728 84.37924 63.52448 27.29 691.11
qbeach | 4728 .2410113 .1906919 .0678 .5333
qpier | 4728 .1622237 .1603389 .0014 .4522
-------------+--------------------------------------------------------
qprivate | 4728 .1712146 .2097219 .0002 .7369
qcharter | 4728 .6293679 .7058901 .0021 2.3101
income | 4728 4099.337 2461.183 416.6667 12500
ydiv1000 | 4728 4.099337 2.461183 .4166667 12.5
d | 4728 .25 .4330585 0 1
-------------+--------------------------------------------------------
p | 4728 86.61996 88.01813 1.29 843.186
q | 4728 .3009544 .4335593 .0002 2.3101
obsnum | 4728 2364.5 1365 1 4728
dummy1 | 4728 .25 .4330585 0 1
dummy2 | 4728 .25 .4330585 0 1
-------------+--------------------------------------------------------
dummy3 | 4728 .25 .4330585 0 1
dummy4 | 4728 .25 .4330585 0 1
d1y | 4728 1.024834 2.160064 0 12.5
d2y | 4728 1.024834 2.160064 0 12.5
d3y | 4728 1.024834 2.160064 0 12.5
-------------+--------------------------------------------------------
d4y | 4728 1.024834 2.160064 0 12.5
.
. * The following gives Mixed column of Table 15.2 p.493
. * Note that dummy1 and d1y are omitted to avoid dummy variablle trap
.
. clogit d dummy2 dummy3 dummy4 d2y d3y d4y p q, group(id)
Iteration 0: log likelihood = -1538.389
Iteration 1: log likelihood = -1297.4143
Iteration 2: log likelihood = -1233.5431
Iteration 3: log likelihood = -1216.8043
Iteration 4: log likelihood = -1215.1582
Iteration 5: log likelihood = -1215.1376
Iteration 6: log likelihood = -1215.1376
Conditional (fixed-effects) logistic regression Number of obs = 4728
LR chi2(8) = 846.92
Prob > chi2 = 0.0000
Log likelihood = -1215.1376 Pseudo R2 = 0.2584
------------------------------------------------------------------------------
d | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
dummy2 | .7779594 .2204939 3.53 0.000 .3457992 1.21012
dummy3 | .5272788 .2227927 2.37 0.018 .0906131 .9639444
dummy4 | 1.694366 .2240506 7.56 0.000 1.255235 2.133497
d2y | -.1275771 .0506395 -2.52 0.012 -.2268288 -.0283255
d3y | .0894398 .0500671 1.79 0.074 -.0086898 .1875695
d4y | -.0332917 .0503409 -0.66 0.508 -.131958 .0653746
p | -.0251166 .0017317 -14.50 0.000 -.0285106 -.0217225
q | .357782 .1097733 3.26 0.001 .1426302 .5729337
------------------------------------------------------------------------------
.
. ******* (2) NESTED LOGIT MODEL (p.511) *********
.
. * Define the Tree for Nested logit
. * with nesting structure
. * / \
. * / \ / \
. * In this case with parameter rho_j differing across alternatives
. * Stata 8 estimates the earlier variant of the nested logit model
. * rather than the preferred variant given in the text.
. * See the discussion at bottom of page 511 and also Train (2003, p.88)
.
. nlogitgen type = alterntv(shore: 1 | 2 , boat: 3 | 4)
new variable type is generated with 2 groups
label list lb_type
lb_type:
1 shore
2 boat
. nlogittree alterntv type
tree structure specified for the nested logit model
top --> bottom
type alterntv
--------------------------
shore 1
2
boat 3
4
.
. *** (2A) Estimate the nested logit model
. *** This is the model on p.511 that has "higher log-likelihood"
.
. * For the top level we use regressors that do not vary at the lower level
. * So not p or q, but could be income or alternative dummy
. * Here use income and alternative dummy
. gen dshore = (type ==1) * 1
. gen dshorey = (type ==1) * ydiv1000
. nlogit d (alterntv = p q) (type = dshore dshorey), group(id)
tree structure specified for the nested logit model
top --> bottom
type alterntv
--------------------------
shore 1
2
boat 3
4
initial: log likelihood = -1256.8179
rescale: log likelihood = -1256.8179
rescale eq: log likelihood = -1228.6278
Iteration 0: log likelihood = -1228.6278
Iteration 1: log likelihood = -1227.407 (backed up)
Iteration 2: log likelihood = -1225.366 (backed up)
Iteration 3: log likelihood = -1216.5831 (backed up)
Iteration 4: log likelihood = -1210.9623
Iteration 5: log likelihood = -1210.323 (backed up)
Iteration 6: log likelihood = -1199.5959
Iteration 7: log likelihood = -1198.2166
Iteration 8: log likelihood = -1193.1834
Iteration 9: log likelihood = -1190.8805
Iteration 10: log likelihood = -1188.0112
Iteration 11: log likelihood = -1185.7944
Iteration 12: log likelihood = -1184.8715
Iteration 13: log likelihood = -1183.776
Iteration 14: log likelihood = -1182.6316
Iteration 15: log likelihood = -1182.1119
Iteration 16: log likelihood = -1181.8783
Iteration 17: log likelihood = -1181.323
Iteration 18: log likelihood = -1181.162
Iteration 19: log likelihood = -1180.912
Iteration 20: log likelihood = -1180.7877
Iteration 21: log likelihood = -1180.5545
Iteration 22: log likelihood = -1180.4177
Iteration 23: log likelihood = -1180.2966
BFGS stepping has contracted, resetting BFGS Hessian (0)
Iteration 24: log likelihood = -1180.2253
Iteration 25: log likelihood = -1180.2209 (backed up)
Iteration 26: log likelihood = -1180.2139 (backed up)
Iteration 27: log likelihood = -1180.2137 (backed up)
Iteration 28: log likelihood = -1180.2113
Iteration 29: log likelihood = -1180.2019
Iteration 30: log likelihood = -1180.1739
Iteration 31: log likelihood = -1180.1278
BFGS stepping has contracted, resetting BFGS Hessian (1)
Iteration 32: log likelihood = -1180.0852
Iteration 33: log likelihood = -1180.0773 (backed up)
Iteration 34: log likelihood = -1180.0762 (backed up)
Iteration 35: log likelihood = -1180.0762 (backed up)
Iteration 36: log likelihood = -1180.0758
Iteration 37: log likelihood = -1180.0694
Iteration 38: log likelihood = -1180.0671
Iteration 39: log likelihood = -1180.0664
BFGS stepping has contracted, resetting BFGS Hessian (2)
Iteration 40: log likelihood = -1180.058
Iteration 41: log likelihood = -1180.0576 (backed up)
Iteration 42: log likelihood = -1180.0575 (backed up)
Iteration 43: log likelihood = -1180.0575 (backed up)
Iteration 44: log likelihood = -1180.0573
Iteration 45: log likelihood = -1180.0466
Iteration 46: log likelihood = -1180.0434
BFGS stepping has contracted, resetting BFGS Hessian (3)
Iteration 47: log likelihood = -1180.043
Iteration 48: log likelihood = -1180.0427 (backed up)
Iteration 49: log likelihood = -1180.0427 (backed up)
Iteration 50: log likelihood = -1180.0427 (backed up)
Iteration 51: log likelihood = -1180.0427
Iteration 52: log likelihood = -1180.0422
BFGS stepping has contracted, resetting BFGS Hessian (4)
Iteration 53: log likelihood = -1180.0414
Iteration 54: log likelihood = -1180.0412 (backed up)
Iteration 55: log likelihood = -1180.0412 (backed up)
Iteration 56: log likelihood = -1180.0412 (backed up)
Iteration 57: log likelihood = -1180.0411
Iteration 58: log likelihood = -1180.0404
Iteration 59: log likelihood = -1180.0401
BFGS stepping has contracted, resetting BFGS Hessian (5)
Iteration 60: log likelihood = -1180.0381
Iteration 61: log likelihood = -1180.038 (backed up)
Iteration 62: log likelihood = -1180.0364 (backed up)
Iteration 63: log likelihood = -1180.0364 (backed up)
Iteration 64: log likelihood = -1180.0364
Iteration 65: log likelihood = -1180.0361
Iteration 66: log likelihood = -1180.0357
BFGS stepping has contracted, resetting BFGS Hessian (6)
Iteration 67: log likelihood = -1180.0348
Iteration 68: log likelihood = -1180.0348 (backed up)
Iteration 69: log likelihood = -1180.0348 (backed up)
Iteration 70: log likelihood = -1180.0348 (backed up)
Iteration 71: log likelihood = -1180.0348
Iteration 72: log likelihood = -1180.0331
Iteration 73: log likelihood = -1180.0328
BFGS stepping has contracted, resetting BFGS Hessian (7)
Iteration 74: log likelihood = -1180.0319
Iteration 75: log likelihood = -1180.0318 (backed up)
Iteration 76: log likelihood = -1180.0317 (backed up)
Iteration 77: log likelihood = -1180.0317 (backed up)
Iteration 78: log likelihood = -1180.0317 (backed up)
Iteration 79: log likelihood = -1180.0313
BFGS stepping has contracted, resetting BFGS Hessian (8)
Iteration 80: log likelihood = -1180.031
Iteration 81: log likelihood = -1180.031 (backed up)
Iteration 82: log likelihood = -1180.031 (backed up)
Iteration 83: log likelihood = -1180.031 (backed up)
Iteration 84: log likelihood = -1180.031 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (9)
Iteration 85: log likelihood = -1180.0305
Iteration 86: log likelihood = -1180.0304 (backed up)
Iteration 87: log likelihood = -1180.0304 (backed up)
Iteration 88: log likelihood = -1180.0304 (backed up)
Iteration 89: log likelihood = -1180.0304
Iteration 90: log likelihood = -1180.0303
Iteration 91: log likelihood = -1180.0301
BFGS stepping has contracted, resetting BFGS Hessian (10)
Iteration 92: log likelihood = -1180.0296
Iteration 93: log likelihood = -1180.0295 (backed up)
Iteration 94: log likelihood = -1180.0295 (backed up)
Iteration 95: log likelihood = -1180.0295 (backed up)
Iteration 96: log likelihood = -1180.0295
Iteration 97: log likelihood = -1180.0292
Iteration 98: log likelihood = -1180.029
BFGS stepping has contracted, resetting BFGS Hessian (11)
Iteration 99: log likelihood = -1180.0288
Iteration 100: log likelihood = -1180.0288 (backed up)
Iteration 101: log likelihood = -1180.0288 (backed up)
Iteration 102: log likelihood = -1180.0288 (backed up)
Iteration 103: log likelihood = -1180.0288 (backed up)
Iteration 104: log likelihood = -1180.0285
BFGS stepping has contracted, resetting BFGS Hessian (12)
Iteration 105: log likelihood = -1180.0283
Iteration 106: log likelihood = -1180.0283 (backed up)
Iteration 107: log likelihood = -1180.0283 (backed up)
Iteration 108: log likelihood = -1180.0283 (backed up)
Iteration 109: log likelihood = -1180.0283
Iteration 110: log likelihood = -1180.0282
Iteration 111: log likelihood = -1180.028
BFGS stepping has contracted, resetting BFGS Hessian (13)
Iteration 112: log likelihood = -1180.0274
Iteration 113: log likelihood = -1180.0274 (backed up)
Iteration 114: log likelihood = -1180.0274 (backed up)
Iteration 115: log likelihood = -1180.0274 (backed up)
Iteration 116: log likelihood = -1180.0274 (backed up)
Iteration 117: log likelihood = -1180.0266
BFGS stepping has contracted, resetting BFGS Hessian (14)
Iteration 118: log likelihood = -1180.0265
Iteration 119: log likelihood = -1180.0265 (backed up)
Iteration 120: log likelihood = -1180.0265 (backed up)
Iteration 121: log likelihood = -1180.0265 (backed up)
Iteration 122: log likelihood = -1180.0265 (backed up)
Iteration 123: log likelihood = -1180.0263
BFGS stepping has contracted, resetting BFGS Hessian (15)
Iteration 124: log likelihood = -1180.0261
Iteration 125: log likelihood = -1180.0261 (backed up)
Iteration 126: log likelihood = -1180.0261 (backed up)
Iteration 127: log likelihood = -1180.0261 (backed up)
Iteration 128: log likelihood = -1180.0261 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (16)
Iteration 129: log likelihood = -1180.026
Iteration 130: log likelihood = -1180.026 (backed up)
Iteration 131: log likelihood = -1180.026 (backed up)
Iteration 132: log likelihood = -1180.026 (backed up)
Iteration 133: log likelihood = -1180.026 (backed up)
Iteration 134: log likelihood = -1180.0259
BFGS stepping has contracted, resetting BFGS Hessian (17)
Iteration 135: log likelihood = -1180.0213
Iteration 136: log likelihood = -1180.0208 (backed up)
Iteration 137: log likelihood = -1180.0207 (backed up)
Iteration 138: log likelihood = -1180.0207 (backed up)
Iteration 139: log likelihood = -1180.0206
Iteration 140: log likelihood = -1180.0191
Iteration 141: log likelihood = -1180.0186
BFGS stepping has contracted, resetting BFGS Hessian (18)
Iteration 142: log likelihood = -1180.0185
Iteration 143: log likelihood = -1180.0185 (backed up)
Iteration 144: log likelihood = -1180.0185 (backed up)
Iteration 145: log likelihood = -1180.0185
Iteration 146: log likelihood = -1180.0185 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (19)
Iteration 147: log likelihood = -1180.0184
Iteration 148: log likelihood = -1180.0184 (backed up)
Iteration 149: log likelihood = -1180.0184 (backed up)
Iteration 150: log likelihood = -1180.0184 (backed up)
Iteration 151: log likelihood = -1180.0184 (backed up)
Iteration 152: log likelihood = -1180.0184
Iteration 153: log likelihood = -1180.0183
BFGS stepping has contracted, resetting BFGS Hessian (20)
Iteration 154: log likelihood = -1180.0177
Iteration 155: log likelihood = -1180.0176 (backed up)
Iteration 156: log likelihood = -1180.0176 (backed up)
Iteration 157: log likelihood = -1180.0176 (backed up)
Iteration 158: log likelihood = -1180.0176 (backed up)
Iteration 159: log likelihood = -1180.0172
Iteration 160: log likelihood = -1180.0171
BFGS stepping has contracted, resetting BFGS Hessian (21)
Iteration 161: log likelihood = -1180.017
Iteration 162: log likelihood = -1180.017 (backed up)
Iteration 163: log likelihood = -1180.017 (backed up)
Iteration 164: log likelihood = -1180.017 (backed up)
Iteration 165: log likelihood = -1180.017
Iteration 166: log likelihood = -1180.017
BFGS stepping has contracted, resetting BFGS Hessian (22)
Iteration 167: log likelihood = -1180.0169
Iteration 168: log likelihood = -1180.0169 (backed up)
Iteration 169: log likelihood = -1180.0169 (backed up)
Iteration 170: log likelihood = -1180.0169 (backed up)
Iteration 171: log likelihood = -1180.0169 (backed up)
Iteration 172: log likelihood = -1180.0169
Iteration 173: log likelihood = -1180.0169
BFGS stepping has contracted, resetting BFGS Hessian (23)
Iteration 174: log likelihood = -1180.0167
Iteration 175: log likelihood = -1180.0167 (backed up)
Iteration 176: log likelihood = -1180.0167 (backed up)
Iteration 177: log likelihood = -1180.0167 (backed up)
Iteration 178: log likelihood = -1180.0167 (backed up)
Iteration 179: log likelihood = -1180.0166
BFGS stepping has contracted, resetting BFGS Hessian (24)
Iteration 180: log likelihood = -1180.0165
Iteration 181: log likelihood = -1180.0165 (backed up)
Iteration 182: log likelihood = -1180.0165 (backed up)
Iteration 183: log likelihood = -1180.0165 (backed up)
Iteration 184: log likelihood = -1180.0165 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (25)
Iteration 185: log likelihood = -1180.0165
Iteration 186: log likelihood = -1180.0165 (backed up)
Iteration 187: log likelihood = -1180.0165 (backed up)
Iteration 188: log likelihood = -1180.0164 (backed up)
Iteration 189: log likelihood = -1180.0164 (backed up)
Iteration 190: log likelihood = -1180.0164
BFGS stepping has contracted, resetting BFGS Hessian (26)
Iteration 191: log likelihood = -1180.0164
Iteration 192: log likelihood = -1180.0164 (backed up)
Iteration 193: log likelihood = -1180.0164 (backed up)
Iteration 194: log likelihood = -1180.0164 (backed up)
Iteration 195: log likelihood = -1180.0164 (backed up)
Iteration 196: log likelihood = -1180.0164
BFGS stepping has contracted, resetting BFGS Hessian (27)
Iteration 197: log likelihood = -1180.0163
Iteration 198: log likelihood = -1180.0163 (backed up)
Iteration 199: log likelihood = -1180.0163 (backed up)
Iteration 200: log likelihood = -1180.0163 (backed up)
Iteration 201: log likelihood = -1180.0163 (backed up)
Iteration 202: log likelihood = -1180.0162
BFGS stepping has contracted, resetting BFGS Hessian (28)
Iteration 203: log likelihood = -1180.0162
Iteration 204: log likelihood = -1180.0162 (backed up)
Iteration 205: log likelihood = -1180.0162 (backed up)
Iteration 206: log likelihood = -1180.0162 (backed up)
Iteration 207: log likelihood = -1180.0162 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (29)
Iteration 208: log likelihood = -1180.0161
Iteration 209: log likelihood = -1180.0161 (backed up)
Iteration 210: log likelihood = -1180.0161 (backed up)
Iteration 211: log likelihood = -1180.0161 (backed up)
Iteration 212: log likelihood = -1180.0161
Iteration 213: log likelihood = -1180.0161
BFGS stepping has contracted, resetting BFGS Hessian (30)
Iteration 214: log likelihood = -1180.016
Iteration 215: log likelihood = -1180.016 (backed up)
Iteration 216: log likelihood = -1180.016 (backed up)
Iteration 217: log likelihood = -1180.016 (backed up)
Iteration 218: log likelihood = -1180.016 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (31)
Iteration 219: log likelihood = -1180.016
Iteration 220: log likelihood = -1180.016 (backed up)
Iteration 221: log likelihood = -1180.016 (backed up)
Iteration 222: log likelihood = -1180.016 (backed up)
Iteration 223: log likelihood = -1180.016 (backed up)
BFGS stepping has contracted, resetting BFGS Hessian (32)
Iteration 224: log likelihood = -1180.0159
Iteration 225: log likelihood = -1180.0159 (backed up)
Iteration 226: log likelihood = -1180.0159 (backed up)
Iteration 227: log likelihood = -1180.0159 (backed up)
Iteration 228: log likelihood = -1180.0159
Iteration 229: log likelihood = -1180.0159
Iteration 230: log likelihood = -1180.0159
BFGS stepping has contracted, resetting BFGS Hessian (33)
Iteration 231: log likelihood = -1180.0157
Iteration 232: log likelihood = -1180.0157 (backed up)
Iteration 233: log likelihood = -1180.0157 (backed up)
Iteration 234: log likelihood = -1180.0157 (backed up)
Iteration 235: log likelihood = -1180.0157 (backed up)
Iteration 236: log likelihood = -1180.0156
Nested logit estimates
Levels = 2 Number of obs = 4728
Dependent variable = d LR chi2(6) = 917.1687
Log likelihood = -1180.0156 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
alterntv |
p | -.0013303 .001081 -1.23 0.218 -.003449 .0007883
q | .1284825 .1038986 1.24 0.216 -.075155 .33212
-------------+----------------------------------------------------------------
type |
dshore | -11.40196 9.15307 -1.25 0.213 -29.34164 6.537733
dshorey | .1108341 .0531049 2.09 0.037 .0067505 .2149178
-------------+----------------------------------------------------------------
(incl. value |
parameters) |
type |
/shore | 29.98591 24.40089 1.23 0.219 -17.83896 77.81078
/boat | 14.06438 11.39886 1.23 0.217 -8.276971 36.40572
------------------------------------------------------------------------------
LR test of homoskedasticity (iv = 1): chi2(2)= 145.39 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
. estimates store nlogitunrest
.
. *** (2B) Estimate the restricted nested logit model
. *** This is the model on p.511 that has log L = -1252
.
. * Set the inclusive value parameters to 1
. nlogit d (alterntv = p q) (type = dshore dshorey), group(id) ivc(shore=1, boat=1)
tree structure specified for the nested logit model
top --> bottom
type alterntv
--------------------------
shore 1
2
boat 3
4
User-defined constraint(s):
IV constraint(s):
[shore]_cons = 1
[boat]_cons = 1
initial: log likelihood = -1256.8179
rescale: log likelihood = -1256.8179
rescale eq: log likelihood = -1228.6278
Iteration 0: log likelihood = -1264.4012
Iteration 1: log likelihood = -1264.1213 (backed up)
Iteration 2: log likelihood = -1256.9241 (backed up)
Iteration 3: log likelihood = -1255.0984 (backed up)
Iteration 4: log likelihood = -1254.4838
Iteration 5: log likelihood = -1252.7216
Iteration 6: log likelihood = -1252.7111
Iteration 7: log likelihood = -1252.711
Nested logit estimates
Levels = 2 Number of obs = 4728
Dependent variable = d LR chi2(4) = 771.7778
Log likelihood = -1252.711 Prob > chi2 = 0.0000
------------------------------------------------------------------------------
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
alterntv |
p | -.020246 .0012832 -15.78 0.000 -.022761 -.017731
q | .7552644 .0918004 8.23 0.000 .575339 .9351899
-------------+----------------------------------------------------------------
type |
dshore | -.5897435 .1565201 -3.77 0.000 -.8965172 -.2829697
dshorey | -.0790869 .0381453 -2.07 0.038 -.1538503 -.0043235
-------------+----------------------------------------------------------------
(incl. value |
parameters) |
type |
/shore | 1 . . . . .
/boat | 1 . . . . .
------------------------------------------------------------------------------
LR test of homoskedasticity (iv = 1): chi2(0)= 0.00 Prob > chi2 = .
------------------------------------------------------------------------------
. estimates store nlogitrest
.
. * Perform a likelihood ratio test that inclusive parameters = 1
. lrtest nlogitunrest nlogitrest
likelihood-ratio test LR chi2(2) = 145.39
(Assumption: nlogitrest nested in nlogitunrest) Prob > chi2 = 0.0000
.
. *** (2C) As a check, verify that this restricted nested logit = conditional logit
.
. clogit d p q dshore dshorey, group(id)
Iteration 0: log likelihood = -1547.6028
Iteration 1: log likelihood = -1317.5764
Iteration 2: log likelihood = -1262.8183
Iteration 3: log likelihood = -1253.096
Iteration 4: log likelihood = -1252.7117
Iteration 5: log likelihood = -1252.711
Conditional (fixed-effects) logistic regression Number of obs = 4728
LR chi2(4) = 771.78
Prob > chi2 = 0.0000
Log likelihood = -1252.711 Pseudo R2 = 0.2355
------------------------------------------------------------------------------
d | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
p | -.0202461 .0012832 -15.78 0.000 -.0227611 -.0177311
q | .7552646 .0918003 8.23 0.000 .5753392 .9351899
dshore | -.5897442 .15652 -3.77 0.000 -.8965178 -.2829706
dshorey | -.0790866 .0381453 -2.07 0.038 -.1538499 -.0043232
------------------------------------------------------------------------------
.
. ********** CLOSE OUTPUT **********
. log close
log: c:\Imbook\bwebpage\Section4\mma15p2gev.txt
log type: text
closed on: 19 May 2005, 12:19:10
----------------------------------------------------------------------------------------------------