------------------------------------------------------------------------------------------------------ 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 ----------------------------------------------------------------------------------------------------