Applied Choice AnalysisThe second edition of this popular book brings students fully up to date with the latest methods and techniques in choice analysis. Comprehensive yet accessible, it offers a unique introduction to anyone interested in understanding how to model and forecast the range of choices made by individuals and groups. In addition to a complete rewrite of several chapters, new topics covered include ordered choice, scaled MNL, generalized mixed logit, latent class models, group decision making, heuristics and attribute processing strategies, expected utility theory, and prospect theoretic applications. Many additional case studies are used to illustrate the applications of choice analysis with extensive command syntax provided for all Nlogit applications and datasets available online. With its unique blend of theory, estimation, and application, this book has broad appeal to all those interested in choice modeling methods and will be a valuable resource for students as well as researchers, professionals, and consultants. |
Contents
| 16 | |
| 30 | |
Families of discrete choice models | 80 |
Estimating discrete choice models | 117 |
Experimental design and choice experiments | 189 |
level range | 270 |
Statistical inference | 320 |
Other matters that analysts often inquire about | 360 |
Nested logit estimation | 560 |
Mixed logit estimation | 601 |
Latent class models | 706 |
Binary choice models | 742 |
Ordered choices | 804 |
Combining sources of data | 836 |
Frontiers of choice analysis | 899 |
Attribute processing heuristics and preference construction | 937 |
Nlogit for applied choice analysis | 387 |
Data set up for Nlogit | 400 |
the workhorse multinomial logit | 437 |
Handling unlabeled discrete choice data | 472 |
Getting more from your model | 492 |
Group decision making | 1072 |
Select glossary | 1116 |
| 1128 | |
| 1163 | |
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Common terms and phrases
actpt*act altij analyst ASCs assumed attribute levels behavioral calculated Chapter choice experiment choice probabilities choice set choice situations choice tasks Coefficient Error columns command computed constant constrained correlation cost covariance covariance matrix cset data set decision makers delta method discrete choice models draws dummy dummy coded effects coding egtpt*egt elasticities Equation Error z z|>Z example Fixed Parameter Halton sequences Hensher heterogeneity individual interaction invcpt*invc invt INVT2 INVTPT invtpt*invt2 latent class light rail likelihood function linear logit model marginal effects marginal utility matrix mean mixed logit MNL model model estimation multinomial logit multivariate normal distribution non-linear normal distribution observed orthogonal design output parameter estimates partial effects preference probit model random parameters respondents RP data sample scale simulated specific standard deviation Standard Prob Table toll treatment combinations trpt*trnf unobserved utility functions variance zero