Applied choice analysis: a primer
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2005
|
| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Hier auch später erschienene, unveränderte Nachdrucke |
| Physical Description: | XXIV, 717 S. Ill., graph. Darst. |
| ISBN: | 0521844266 9780521844260 0521605776 9780521605779 |
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| 100 | 1 | |a Hensher, David A. |d 1947- |e Verfasser |0 (DE-588)142773298 |4 aut | |
| 245 | 1 | 0 | |a Applied choice analysis |b a primer |c David A. Hensher ; John M. Rose ; William H. Greene |
| 250 | |a 1. publ. | ||
| 264 | 1 | |a Cambridge [u.a.] |b Cambridge Univ. Press |c 2005 | |
| 300 | |a XXIV, 717 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Hier auch später erschienene, unveränderte Nachdrucke | ||
| 650 | 7 | |a Besluitvorming |2 gtt | |
| 650 | 7 | |a Keuzegedrag |2 gtt | |
| 650 | 4 | |a Prise de décision - Modèles mathématiques | |
| 650 | 4 | |a Mathematisches Modell | |
| 650 | 4 | |a Decision making |x Mathematical models | |
| 650 | 0 | 7 | |a Statistische Entscheidungstheorie |0 (DE-588)4077850-2 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Statistische Entscheidungstheorie |0 (DE-588)4077850-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Rose, John M. |e Verfasser |0 (DE-588)130335355 |4 aut | |
| 700 | 1 | |a Greene, William |d 1951- |e Verfasser |0 (DE-588)124700551 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bayreuth |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014742130&sequence=000005&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-014742130 | ||
Record in the Search Index
| _version_ | 1804135269339758592 |
|---|---|
| adam_text | Contents
List of figures page
xiii
List of tables
xviii
Preface
xxiii
Part I Basic topics
1
In the beginning
3
2
Basic notions of statistics
8
2.1
Introduction
8
2.2
Data
8
2.2.1
The importance of understanding data
10
2.3
A note on mathematical notation
10
2.3.1
Summation
11
2.3.2
Product
12
2.4
Probability
12
2.4.1
Relative frequencies
13
2.4.2
Defining random variables
14
2.4.3
Probability distribution functions
14
2.4.4
Cumulative distribution functions
16
2.4.5
Multivariate probability density functions
17
2.4.6
The multivariate probability function
18
2.4.7
Marginal probability density functions
21
2.4.8
Conditional probability density functions
21
2.4.9
Defining statistical independence
23
2.5
Properties of random variables
23
2.5.1
Expected value
25
2.5.1.1
Properties of expected values
26
2.5.2
Variance
28
2.5.2.1
Properties of variance
28
vi
Contents
2.5.3
Covariance
ЗО
2.5.3.1
Properties of covariance
31
2.5.4
The variance-covariance matrix
32
2.5.5
Correlation
33
2.5.5.1
Properties of the correlation coefficient
34
2.5.6
Correlation and variances
36
2.6
Sample population statistics
36
2.6.1
The sample mean
36
2.6.2
The sample variance
38
2.6.3
The sample covariance
38
2.6.4
The sample correlation coefficient
39
2.7
Sampling error and sampling distributions
39
2.8
Hypothesis testing
41
2.8.1
Defining the null and alternative hypotheses
42
2.8.2
Selecting the test-statistic
44
2.8.3
Significance of the test and alpha
45
2.8.4
Performing the test
51
2.8.5
Example hypothesis test: the one sample f-test
51
2.9
Matrix algebra
52
2.9.1
Transposition
53
2.9.2
Matrix addition and subtraction
53
2.9.3
Matrix multiplication by a scalar
54
2.9.4
Matrix multiplication
54
2.9.5
Determinants of matrices
55
2.9.6
The identity matrix
56
2.9.7
The inverse of a matrix
57
2.9.8
Linear and quadratic forms
58
2.9.9
Positive definite and negative definite matrices
59
2.10
Conclusion
59
Appendix 2A Measures of correlation or similarity
59
Choosing
62
3.1
Introduction
62
3.2
Individuals have preferences, and they count
63
3.3
Using knowledge of preferences and constraints in choice analysis
71
3.4
Setting up a behavioral choice rale
74
3.5
Deriving a basic choice model
82
3.6
Concluding overview
86
Paradigms of choice data
88
4.1
Introduction
88
4.2
Data consistent with choice
89
4.3
Revealed preference data
92
4.3.1
Choice-based sampling
95
Contents
VU
4.4
Stated preference (or stated choice) data
96
4.5
Further comparisons
97
4.6
Why not use both RP and SP data?
98
4.7
Socio-demographic characteristic data
98
Processes in setting up stated choice experiments
100
5.1
Introduction
100
5.2
What is an experimental design?
100
5.2.1
Stage
1 :
Problem definition refinement
103
5.2.2
Stage
2:
Stimuli refinement
104
5.2.2.1
Refining the list of alternatives
104
5.2.2.2
Refining the list of attributes and attribute levels
105
5.2.3
Stage
3:
Experimental design considerations
109
5.2.3.1
Labeled versus unlabeled experiments
112
5.2.3.2
Reducing the number of levels
114
5.2.3.3
Reducing the size of experimental designs
115
5.2.3.4
Dummy and effects coding
119
5.2.3.5
Calculating the degrees of freedom required
122
5.2.3.6
Blocking the design
126
5.2.4
Stage
4:
Generating experimental designs
127
5.2.4.1
Assigning an attribute as a blocking variable
130
5.2.5
Stage
5:
Allocating attributes to design columns
131
5.3
A note on unlabeled experimental designs
150
5.4
Optimal designs
152
Appendix
5
A Designing nested attributes
154
Appendix
5
В
Assignment of quantitative attribute-level labels
156
Choices in data collection
161
6.1
Introduction
161
6.2
General survey instrument construction
161
6.3
Questionnaires for choice data
166
6.3.1
Stage
6:
Generation of choice sets
166
6.3.2
Stage
7:
Randomizing choice sets
170
6.3.3
Stage
8:
Survey construction
172
6.3.3.1
Choice context
173
6.3.3.2
Use an example
174
6.3.3.3
Independence of choice sets
174
6.3.3.4
More than one choice
175
6.3.3.5
The no-choice or delay-choice alternative
176
6.4
Revealed preferences in questionnaires
177
6.5
Studies involving both RP and SP data
177
6.6
Using RP data in SP experiments: the current alternative
178
6.7
Sampling for choice data: the theory
184
6.7.1
Simple random samples
185
VÍii
Contents
6.7.2
Stratified random sampling
190
6.7.3
Conclusion to the theory of calculating sample sizes
192
6.8
Sampling for choice data: the reality
193
NLOGIT for applied choice analysis: a primer
197
7.1
Introduction
197
7.2
About the software
197
7.2.1
About NLOGIT
197
7.2.2
About NLOGIT/AC A
198
7.2.3
Installing NLOGIT/
АСА
198
7.3
Starting NLOGIT/
АСА
and exiting after a session
198
7.3.1
Starting the program
198
7.3.2
Inputting the data
198
7.3.3
Reading data
200
7.3.4
The project file
200
7.3.5
Leaving your session
201
7.4
Using NLOGIT
201
7.5
How to get NLOGIT to do what you want
202
7.5.1
Using the Text Editor
202
7.5.2
Command format
204
7.5.3
Commands
205
7.5.4
Using the Project File Box
206
7.6
Useful hints and tips
206
7.6.1
Limitations in NLOGIT (and NLOGIT/
АСА)
207
7.7
NLOGIT software
207
7.7.1
Support
208
7.7.2
The program installed on your computer
208
7.7.3
Using NLOGIT/
АСА
in the remainder of the book
208
Appendix 7A Diagnostic and error messages
208
Handling choice data
218
8.1
Introduction
218
8.2
The basic data setup
219
8.2.1
Entering multiple data sets: stacking and melding
222
8.2.2
Handling data on the non-chosen alternative in RP data
222
8.2.3
Combining sources of data
224
8.2.4
Weighting on an exogenous variable
226
8.2.5
Handling rejection: the no option
227
8.3
Entering data into NLOGIT
230
8.3.1
Entering data directly into NLOGIT
230
8.3.2
Importing data into NLOGIT
232
8.3.2.1
The Text/Document Editor
232
8.3.3
Reading data into NLOGIT
232
8.3.4
Writing data into NLOGIT
235
8.3.5
Saving data sets
235
Contents
IX
8.3.6
Loading data into NLOGIT
236
8.3.6.1
Changing the maximum default size of the
Data Editor
236
8.4
Data entered into a single line
237
8.5
Data cleaning
241
8.5.1
Testing for multicollinearity using NLOGIT
246
Appendix 8A Design effects coding
248
Appendix 8B Converting single-line data commands
250
9
Case study: mode-choice data
254
9.1
Introduction
254
9.2
Study objectives
254
9.3
The pilot study
256
9.3.1
Pilot sample collection
263
9.3.1.1
Interviewer briefing
263
9.3.1.2
Interviewing
264
9.3.1.3
Analysis of contacts
264
9.3.1.4
Interviewer debriefing
265
9.4
The main survey
265
9.4.1
The mode-choice experiment
267
9.4.1.1
Detailed description of attributes
274
9.4.1.2
Using the showcards
276
9.4.2
RP data
276
9.4.3
The household questionnaire
277
9.4.4
The commuter questionnaire
277
9.4.5
The sample
278
9.4.5.1
Screening respondents
282
9.4.5.2
Interviewer briefing
283
9.4.5.3
Interviewing
283
9.4.5.4
Analysis of total contacts
283
9.4.5.5
Questionnaire check edit
284
9.4.5.6
Coding and check edit
284
9.4.5.7
Data entry
286
9.4.5.8
SPSS setup
286
9.5
The case study data
286
9.5.1
Formatting data in NLOGIT
289
9.5.2
Getting to know and cleaning the data
292
Appendix 9A The contextual statement associated with the travel
choice experiment
296
Appendix 9B Mode-choice case study data dictionary
298
Appendix 9C Mode-choice case study variable labels
302
10
Getting started modeling: the basic
MNL
model
308
10.1
Introduction
308
10.2
Modeling choice in NLOGIT: the MNL command
308
1С Contents
10.3
Interpreting the MNL model output
316
10.3.1
Maximum likelihood estimation
317
10.3.2
Determining the sample size and weighting criteria used
323
10.3.3
Interpreting the number of iterations to model convergence
3 24
10.3.4
Determining overall model significance
326
10.3.5
Comparing two models
335
10.3.6
Determining model fit: the
pseudo-/?2
337
10.3.7
Type of response and bad data
339
10.3.8
Obtaining estimates of the indirect utility functions
339
10.3.8.1
Matrix: LastDsta/LastOutput
343
10.4
Interpreting parameters for effects and dummy coded variables
344
10.5
Handling interactions in choice models
352
10.6
Measures of willingness to pay
357
10.7
Obtaining choice probabilities for the sample
360
10.8
Obtaining the utility estimates for the sample
366
Appendix 10A Handling unlabelled experiments
371
11
Getting more from your model
374
11.1
Introduction
374
11.2
Adding to our understanding of the data
375
11.2.1
Show
375
11.2.2
Descriptives
378
11.2.3
Crosstab
381
11.3
Adding to our understanding of the model parameters
383
11.3.1
;Effects: elasticities
384
11.3.2
Calculating arc elasticities
392
11.3.3
;Effects: marginal effects
393
11.4
Simulation
399
11.4.1
Marginal effects for categorical coded variables
407
11.4.2
Reporting marginal effects
411
11.5
Weighting
413
11.5.1
Endogenous weighting
413
11.5.2
Weighting on an exogenous variable
418
11.6
Calibrating the alternative-specific constants of choice models
estimated on SP data
420
11.6.1
Example
(1)
(the market shares of all alternatives are
known a priori)
423
11.6.2
Example
(2)
(the market shares for some alternatives
are unknown)
424
Appendix
11
A Calculating arc elasticities
426
12
Practical issues in the application of choice models
437
12.1
Introduction
437
12.2
Calibration of a choice model for base and forecast years
438
12.3
Designing a population data base: synthetic observations
439
Contents
XI
12.4
The concept
of synthetic households
440
12.4.1
Synthetic households generation framework
441
12.5
The population profiler
443
12.5.1
The synthetic household specification
444
12.6
The sample profiler
448
12.7
Establishing attribute levels associated with choice alternatives
in the base year and in a forecast year
451
12.8
Bringing the components together in the application phase
452
12.9
Developing a decision support system
453
12.9.1
Using the data sample averages in creating the DSS
455
12.9.2
Using the choice data in creating the DSS
461
12.9.3
Improving the look of the DSS
472
12.9.4
Using the DSS
475
12.10
Conclusion
475
Part II Advanced topics
13
Allowing for similarity of alternatives
479
13.1
Introduction
479
13.2
Moving away from IID between all alternatives
481
13.3
Setting out the key relationships for establishing a nested logit
model
482
13.4
The importance of a behaviorally meaningful linkage mechanism
between the branches on a nested structure
486
13.5
The scale parameter
487
13.6
Bounded range for the IV parameter
490
13.7
Searching for the best tree structure
494
Appendix
1
ЗА
Technical details of the nested logit model
495
14
Nested logit estimation
518
14.1
Introduction
518
14.2
The Hausman-test of the IIA assumption
519
14.3
The nested logit model commands
530
14.3.1
Normalizing and constraining IV parameters
534
14.3.2
RUI andRU2
538
14.3.3
Specifying start values for the
N L
model
538
14.3.4
A quick review of the NL model
539
14.4
Estimating an NL model and interpreting the output
541
14.4.1
Estimating the probabilities of a two-level NL model
549
14.4.2
Comparing
RUI
to RU2
556
14.5
Specifying utility functions at higher levels of the NL tree
564
14.6
Handling degenerate branches in NL models
570
14.7
Three-level NL models
574
14.8
Searching for the best NL tree structure: the degenerate nested logit
577
14.9
Combining sources of data: SP-RP
580
XÜ Contents
14.10
Additional commands
592
Appendix 14A The Hausman-test of the IIA assumption for models with
alternative-specific parameter estimates
595
Appendix 14B Three-level NL model system of equations
601
15
The mixed logit model
605
15.1
Introduction
605
15.2
Mixed logit choice models
606
15.3
Conditional distribution for sub-populations with common choices
610
15.4
Model specification issues
611
15.4.1
Selecting the random parameters
611
15.4.2
Selecting the distribution of the random parameters
612
15.4.3
Imposing constraints on a distribution
614
15.4.4
Selecting the number of points for the simulations
614
15.4.5
Preference heterogeneity around the mean of a random
parameter
617
15.4.6
Accounting for observations drawn from the same individual
correlated choice situations
617
15.4.7
Accounting for correlation between parameters
619
15.5
Willingness-to-pay challenges
620
15.6
Conclusions
621
16
Mixed logit estimation
623
16.1
Introduction
623
16.2
The mixed logit model basic commands
623
16.3
NLOGIT output: interpreting the mixed logit model
627
16.4
How can we use random parameter estimates?
635
16.4.1
A note on using the
lognormal
distribution
641
16.5
Imposing constraints on a distribution
645
16.6
Revealing preference heterogeneity around the mean of a
random parameter
650
16.6.1
Using the non-stochastic distribution
656
16.6.2
Handling insignificant heterogeneity around the mean
parameter estimates
661
16.7
Correlated parameters
667
16.8
Common-choice-specific parameter estimates: conditional
parameters
679
16.9
Presenting the distributional outputs graphically using a kernel
density estimator
684
16.10
Willingness-to-pay issues and the mixed logit model
686
Glossary
695
References
710
Index
714
|
| adam_txt |
Contents
List of figures page
xiii
List of tables
xviii
Preface
xxiii
Part I Basic topics
1
In the beginning
3
2
Basic notions of statistics
8
2.1
Introduction
8
2.2
Data
8
2.2.1
The importance of understanding data
10
2.3
A note on mathematical notation
10
2.3.1
Summation
11
2.3.2
Product
12
2.4
Probability
12
2.4.1
Relative frequencies
13
2.4.2
Defining random variables
14
2.4.3
Probability distribution functions
14
2.4.4
Cumulative distribution functions
16
2.4.5
Multivariate probability density functions
17
2.4.6
The multivariate probability function
18
2.4.7
Marginal probability density functions
21
2.4.8
Conditional probability density functions
21
2.4.9
Defining statistical independence
23
2.5
Properties of random variables
23
2.5.1
Expected value
25
2.5.1.1
Properties of expected values
26
2.5.2
Variance
28
2.5.2.1
Properties of variance
28
vi
Contents
2.5.3
Covariance
ЗО
2.5.3.1
Properties of covariance
31
2.5.4
The variance-covariance matrix
32
2.5.5
Correlation
33
2.5.5.1
Properties of the correlation coefficient
34
2.5.6
Correlation and variances
36
2.6
Sample population statistics
36
2.6.1
The sample mean
36
2.6.2
The sample variance
38
2.6.3
The sample covariance
38
2.6.4
The sample correlation coefficient
39
2.7
Sampling error and sampling distributions
39
2.8
Hypothesis testing
41
2.8.1
Defining the null and alternative hypotheses
42
2.8.2
Selecting the test-statistic
44
2.8.3
Significance of the test and alpha
45
2.8.4
Performing the test
51
2.8.5
Example hypothesis test: the one sample f-test
51
2.9
Matrix algebra
52
2.9.1
Transposition
53
2.9.2
Matrix addition and subtraction
53
2.9.3
Matrix multiplication by a scalar
54
2.9.4
Matrix multiplication
54
2.9.5
Determinants of matrices
55
2.9.6
The identity matrix
56
2.9.7
The inverse of a matrix
57
2.9.8
Linear and quadratic forms
58
2.9.9
Positive definite and negative definite matrices
59
2.10
Conclusion
59
Appendix 2A Measures of correlation or similarity
59
Choosing
62
3.1
Introduction
62
3.2
Individuals have preferences, and they count
63
3.3
Using knowledge of preferences and constraints in choice analysis
71
3.4
Setting up a behavioral choice rale
74
3.5
Deriving a basic choice model
82
3.6
Concluding overview
86
Paradigms of choice data
88
4.1
Introduction
88
4.2
Data consistent with choice
89
4.3
Revealed preference data
92
4.3.1
Choice-based sampling
95
Contents
VU
4.4
Stated preference (or stated choice) data
96
4.5
Further comparisons
97
4.6
Why not use both RP and SP data?
98
4.7
Socio-demographic characteristic data
98
Processes in setting up stated choice experiments
100
5.1
Introduction
100
5.2
What is an experimental design?
100
5.2.1
Stage
1 :
Problem definition refinement
103
5.2.2
Stage
2:
Stimuli refinement
104
5.2.2.1
Refining the list of alternatives
104
5.2.2.2
Refining the list of attributes and attribute levels
105
5.2.3
Stage
3:
Experimental design considerations
109
5.2.3.1
Labeled versus unlabeled experiments
112
5.2.3.2
Reducing the number of levels
114
5.2.3.3
Reducing the size of experimental designs
115
5.2.3.4
Dummy and effects coding
119
5.2.3.5
Calculating the degrees of freedom required
122
5.2.3.6
Blocking the design
126
5.2.4
Stage
4:
Generating experimental designs
127
5.2.4.1
Assigning an attribute as a blocking variable
130
5.2.5
Stage
5:
Allocating attributes to design columns
131
5.3
A note on unlabeled experimental designs
150
5.4
Optimal designs
152
Appendix
5
A Designing nested attributes
154
Appendix
5
В
Assignment of quantitative attribute-level labels
156
Choices in data collection
161
6.1
Introduction
161
6.2
General survey instrument construction
161
6.3
Questionnaires for choice data
166
6.3.1
Stage
6:
Generation of choice sets
166
6.3.2
Stage
7:
Randomizing choice sets
170
6.3.3
Stage
8:
Survey construction
172
6.3.3.1
Choice context
173
6.3.3.2
Use an example
174
6.3.3.3
Independence of choice sets
174
6.3.3.4
More than one choice
175
6.3.3.5
The no-choice or delay-choice alternative
176
6.4
Revealed preferences in questionnaires
177
6.5
Studies involving both RP and SP data
177
6.6
Using RP data in SP experiments: the "current alternative"
178
6.7
Sampling for choice data: the theory
184
6.7.1
Simple random samples
185
VÍii
Contents
6.7.2
Stratified random sampling
190
6.7.3
Conclusion to the theory of calculating sample sizes
192
6.8
Sampling for choice data: the reality
193
NLOGIT for applied choice analysis: a primer
197
7.1
Introduction
197
7.2
About the software
197
7.2.1
About NLOGIT
197
7.2.2
About NLOGIT/AC A
198
7.2.3
Installing NLOGIT/
АСА
198
7.3
Starting NLOGIT/
АСА
and exiting after a session
198
7.3.1
Starting the program
198
7.3.2
Inputting the data
198
7.3.3
Reading data
200
7.3.4
The project file
200
7.3.5
Leaving your session
201
7.4
Using NLOGIT
201
7.5
How to get NLOGIT to do what you want
202
7.5.1
Using the Text Editor
202
7.5.2
Command format
204
7.5.3
Commands
205
7.5.4
Using the Project File Box
206
7.6
Useful hints and tips
206
7.6.1
Limitations in NLOGIT (and NLOGIT/
АСА)
207
7.7
NLOGIT software
207
7.7.1
Support
208
7.7.2
The program installed on your computer
208
7.7.3
Using NLOGIT/
АСА
in the remainder of the book
208
Appendix 7A Diagnostic and error messages
208
Handling choice data
218
8.1
Introduction
218
8.2
The basic data setup
219
8.2.1
Entering multiple data sets: stacking and melding
222
8.2.2
Handling data on the non-chosen alternative in RP data
222
8.2.3
Combining sources of data
224
8.2.4
Weighting on an exogenous variable
226
8.2.5
Handling rejection: the "no option"
227
8.3
Entering data into NLOGIT
230
8.3.1
Entering data directly into NLOGIT
230
8.3.2
Importing data into NLOGIT
232
8.3.2.1
The Text/Document Editor
232
8.3.3
Reading data into NLOGIT
232
8.3.4
Writing data into NLOGIT
235
8.3.5
Saving data sets
235
Contents
IX
8.3.6
Loading data into NLOGIT
236
8.3.6.1
Changing the maximum default size of the
Data Editor
236
8.4
Data entered into a single line
237
8.5
Data cleaning
241
8.5.1
Testing for multicollinearity using NLOGIT
246
Appendix 8A Design effects coding
248
Appendix 8B Converting single-line data commands
250
9
Case study: mode-choice data
254
9.1
Introduction
254
9.2
Study objectives
254
9.3
The pilot study
256
9.3.1
Pilot sample collection
263
9.3.1.1
Interviewer briefing
263
9.3.1.2
Interviewing
264
9.3.1.3
Analysis of contacts
264
9.3.1.4
Interviewer debriefing
265
9.4
The main survey
265
9.4.1
The mode-choice experiment
267
9.4.1.1
Detailed description of attributes
274
9.4.1.2
Using the showcards
276
9.4.2
RP data
276
9.4.3
The household questionnaire
277
9.4.4
The commuter questionnaire
277
9.4.5
The sample
278
9.4.5.1
Screening respondents
282
9.4.5.2
Interviewer briefing
283
9.4.5.3
Interviewing
283
9.4.5.4
Analysis of total contacts
283
9.4.5.5
Questionnaire check edit
284
9.4.5.6
Coding and check edit
284
9.4.5.7
Data entry
286
9.4.5.8
SPSS setup
286
9.5
The case study data
286
9.5.1
Formatting data in NLOGIT
289
9.5.2
Getting to know and cleaning the data
292
Appendix 9A The contextual statement associated with the travel
choice experiment
296
Appendix 9B Mode-choice case study data dictionary
298
Appendix 9C Mode-choice case study variable labels
302
10
Getting started modeling: the basic
MNL
model
308
10.1
Introduction
308
10.2
Modeling choice in NLOGIT: the MNL command
308
1С Contents
10.3
Interpreting the MNL model output
316
10.3.1
Maximum likelihood estimation
317
10.3.2
Determining the sample size and weighting criteria used
323
10.3.3
Interpreting the number of iterations to model convergence
3 24
10.3.4
Determining overall model significance
326
10.3.5
Comparing two models
335
10.3.6
Determining model fit: the
pseudo-/?2
337
10.3.7
Type of response and bad data
339
10.3.8
Obtaining estimates of the indirect utility functions
339
10.3.8.1
Matrix: LastDsta/LastOutput
343
10.4
Interpreting parameters for effects and dummy coded variables
344
10.5
Handling interactions in choice models
352
10.6
Measures of willingness to pay
357
10.7
Obtaining choice probabilities for the sample
360
10.8
Obtaining the utility estimates for the sample
366
Appendix 10A Handling unlabelled experiments
371
11
Getting more from your model
374
11.1
Introduction
374
11.2
Adding to our understanding of the data
375
11.2.1
Show
375
11.2.2
Descriptives
378
11.2.3
Crosstab
381
11.3
Adding to our understanding of the model parameters
383
11.3.1
;Effects: elasticities
384
11.3.2
Calculating arc elasticities
392
11.3.3
;Effects: marginal effects
393
11.4
Simulation
399
11.4.1
Marginal effects for categorical coded variables
407
11.4.2
Reporting marginal effects
411
11.5
Weighting
413
11.5.1
Endogenous weighting
413
11.5.2
Weighting on an exogenous variable
418
11.6
Calibrating the alternative-specific constants of choice models
estimated on SP data
420
11.6.1
Example
(1)
(the market shares of all alternatives are
known a priori)
423
11.6.2
Example
(2)
(the market shares for some alternatives
are unknown)
424
Appendix
11
A Calculating arc elasticities
426
12
Practical issues in the application of choice models
437
12.1
Introduction
437
12.2
Calibration of a choice model for base and forecast years
438
12.3
Designing a population data base: synthetic observations
439
Contents
XI
12.4
The concept
of synthetic households
440
12.4.1
Synthetic households'generation framework
441
12.5
The population profiler
443
12.5.1
The synthetic household specification
444
12.6
The sample profiler
448
12.7
Establishing attribute levels associated with choice alternatives
in the base year and in a forecast year
451
12.8
Bringing the components together in the application phase
452
12.9
Developing a decision support system
453
12.9.1
Using the data sample averages in creating the DSS
455
12.9.2
Using the choice data in creating the DSS
461
12.9.3
Improving the look of the DSS
472
12.9.4
Using the DSS
475
12.10
Conclusion
475
Part II Advanced topics
13
Allowing for similarity of alternatives
479
13.1
Introduction
479
13.2
Moving away from IID between all alternatives
481
13.3
Setting out the key relationships for establishing a nested logit
model
482
13.4
The importance of a behaviorally meaningful linkage mechanism
between the branches on a nested structure
486
13.5
The scale parameter
487
13.6
Bounded range for the IV parameter
490
13.7
Searching for the "best" tree structure
494
Appendix
1
ЗА
Technical details of the nested logit model
495
14
Nested logit estimation
518
14.1
Introduction
518
14.2
The Hausman-test of the IIA assumption
519
14.3
The nested logit model commands
530
14.3.1
Normalizing and constraining IV parameters
534
14.3.2
RUI andRU2
538
14.3.3
Specifying start values for the
N L
model
538
14.3.4
A quick review of the NL model
539
14.4
Estimating an NL model and interpreting the output
541
14.4.1
Estimating the probabilities of a two-level NL model
549
14.4.2
Comparing
RUI
to RU2
556
14.5
Specifying utility functions at higher levels of the NL tree
564
14.6
Handling degenerate branches in NL models
570
14.7
Three-level NL models
574
14.8
Searching for the best NL tree structure: the degenerate nested logit
577
14.9
Combining sources of data: SP-RP
580
XÜ Contents
14.10
Additional commands
592
Appendix 14A The Hausman-test of the IIA assumption for models with
alternative-specific parameter estimates
595
Appendix 14B Three-level NL model system of equations
601
15
The mixed logit model
605
15.1
Introduction
605
15.2
Mixed logit choice models
606
15.3
Conditional distribution for sub-populations with common choices
610
15.4
Model specification issues
611
15.4.1
Selecting the random parameters
611
15.4.2
Selecting the distribution of the random parameters
612
15.4.3
Imposing constraints on a distribution
614
15.4.4
Selecting the number of points for the simulations
614
15.4.5
Preference heterogeneity around the mean of a random
parameter
617
15.4.6
Accounting for observations drawn from the same individual
correlated choice situations
617
15.4.7
Accounting for correlation between parameters
619
15.5
Willingness-to-pay challenges
620
15.6
Conclusions
621
16
Mixed logit estimation
623
16.1
Introduction
623
16.2
The mixed logit model basic commands
623
16.3
NLOGIT output: interpreting the mixed logit model
627
16.4
How can we use random parameter estimates?
635
16.4.1
A note on using the
lognormal
distribution
641
16.5
Imposing constraints on a distribution
645
16.6
Revealing preference heterogeneity around the mean of a
random parameter
650
16.6.1
Using the non-stochastic distribution
656
16.6.2
Handling insignificant heterogeneity around the mean
parameter estimates
661
16.7
Correlated parameters
667
16.8
Common-choice-specific parameter estimates: conditional
parameters
679
16.9
Presenting the distributional outputs graphically using a kernel
density estimator
684
16.10
Willingness-to-pay issues and the mixed logit model
686
Glossary
695
References
710
Index
714 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Hensher, David A. 1947- Rose, John M. Greene, William 1951- |
| author_GND | (DE-588)142773298 (DE-588)130335355 (DE-588)124700551 |
| author_facet | Hensher, David A. 1947- Rose, John M. Greene, William 1951- |
| author_role | aut aut aut |
| author_sort | Hensher, David A. 1947- |
| author_variant | d a h da dah j m r jm jmr w g wg |
| building | Verbundindex |
| bvnumber | BV021525699 |
| callnumber-first | Q - Science |
| callnumber-label | QA279 |
| callnumber-raw | QA279.4 |
| callnumber-search | QA279.4 |
| callnumber-sort | QA 3279.4 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 234 |
| classification_tum | MAT 624f |
| ctrlnum | (OCoLC)60559355 (DE-599)BVBBV021525699 |
| dewey-full | 658.4033 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4033 |
| dewey-search | 658.4033 |
| dewey-sort | 3658.4033 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Mathematik Wirtschaftswissenschaften |
| discipline_str_mv | Mathematik Wirtschaftswissenschaften |
| edition | 1. publ. |
| format | Book |
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| index_date | 2024-07-02T14:23:51Z |
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| language | English |
| lccn | 2005283901 |
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| spelling | Hensher, David A. 1947- Verfasser (DE-588)142773298 aut Applied choice analysis a primer David A. Hensher ; John M. Rose ; William H. Greene 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2005 XXIV, 717 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hier auch später erschienene, unveränderte Nachdrucke Besluitvorming gtt Keuzegedrag gtt Prise de décision - Modèles mathématiques Mathematisches Modell Decision making Mathematical models Statistische Entscheidungstheorie (DE-588)4077850-2 gnd rswk-swf Statistische Entscheidungstheorie (DE-588)4077850-2 s DE-604 Rose, John M. Verfasser (DE-588)130335355 aut Greene, William 1951- Verfasser (DE-588)124700551 aut Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014742130&sequence=000005&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hensher, David A. 1947- Rose, John M. Greene, William 1951- Applied choice analysis a primer Besluitvorming gtt Keuzegedrag gtt Prise de décision - Modèles mathématiques Mathematisches Modell Decision making Mathematical models Statistische Entscheidungstheorie (DE-588)4077850-2 gnd |
| subject_GND | (DE-588)4077850-2 |
| title | Applied choice analysis a primer |
| title_auth | Applied choice analysis a primer |
| title_exact_search | Applied choice analysis a primer |
| title_exact_search_txtP | Applied choice analysis a primer |
| title_full | Applied choice analysis a primer David A. Hensher ; John M. Rose ; William H. Greene |
| title_fullStr | Applied choice analysis a primer David A. Hensher ; John M. Rose ; William H. Greene |
| title_full_unstemmed | Applied choice analysis a primer David A. Hensher ; John M. Rose ; William H. Greene |
| title_short | Applied choice analysis |
| title_sort | applied choice analysis a primer |
| title_sub | a primer |
| topic | Besluitvorming gtt Keuzegedrag gtt Prise de décision - Modèles mathématiques Mathematisches Modell Decision making Mathematical models Statistische Entscheidungstheorie (DE-588)4077850-2 gnd |
| topic_facet | Besluitvorming Keuzegedrag Prise de décision - Modèles mathématiques Mathematisches Modell Decision making Mathematical models Statistische Entscheidungstheorie |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014742130&sequence=000005&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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