离散选择模型logit模型实例stata分析

1、Discrete Choice AnalysisTerm paperLogit modeling with stata2016. 12. 14G201538010 LI TINGTINGContents Introduction SP Data set information Modeling scenarios setting Data modification Modeling estimated results Modeling comparison ConclusionIntroduction This paper developed a disaggregated logistics

2、 demand models using discrete choice analysis method. Data used is 2008-SP data from a survey. Stata was employed for the estimation of logit models.SP Data set information Stated pointed: 2007 Analysis commodity: steel Analysis range: 30 people(row 600-1500) Dependent variable: choice Independent v

3、ariables: cost log(#/105) time hour LOS #/10Modeling Scenarios SettingModelingscenarios MarketSegment?YNMODEL 1-1 - Distance 5/63variables (cost/time/LOS)MODEL 2-1 - Distance 5/62variables (cost/time)MODEL 3-1 - Distance 5/62variables (cost/LOS)MODEL 4-1 - Distance 5/62variables (time/LOS)MODEL 1-23

4、variables (cost/time/LOS)MODEL 2-22variables (cost/time)MODEL 3-22variables (cost/LOS)MODEL 4-2 2variables (time/LOS)Data Modification We modify row-data to remove unreasonable data set- Such as the choice of the not-dominant alternative Model 1 has 1 unreasonable data sets(in all data sets) Model 2

5、 has 31 unreasonable data sets(in all data sets) Model 3 has 8 unreasonable data sets(in all data sets) Model 4 has 85 unreasonable data sets(in all data sets)Model 4 is ignoredididmodemodedistancedistanceqtypeqtypecostcosttimetimelosloschoicechoicelogcost5logcost5los2los2223223railrail5 51 12100002

6、10000141460600 00.7419370.7419376 6223223trucktruck5 51 12800002800006 660601 11.0296191.0296196 6223223railrail5 5101021000021000019191001000 00.7419370.7419371010223223trucktruck5 510102800002800007 760601 11.0296191.0296196 6Data reorganize exampleididdistancedistancequestion typequestion typerai

7、lrailtrucktruckchoicechoicecostcosttimetimelosloscostcosttimetimeloslos2232235 51 1210000210000141460 60 2800002800006 660 60 2 22232235 52 2260000260000141480 80 3500003500007 760 60 2 2Basic dataData ModificationModeling Estimated Results(DIST5)Model distance51-1-50.28992-1-50.28843-1-50.1042Model

8、ing Estimated Results(DIST6)Model distance61-1-60.25882-1-60.25393-1-60.0704Modeling Estimated Results(DIST5&6)ModelDistance5&61-20.25392-20.25213-20.0838Modeling Comparison00.050.10.150.20.250.30.35m1-1-5m2-1-5m3-1-5m1-1-6m2-1-6m3-1-6m1-1m2-1m3-1Conclusion The best model is Model 1-1 Statistical results of the estimation indicate that market segmentation makes the model more comprehensive. The more variables model has,