Index. Cambridge University Press Applied Choice Analysis: Second Edition David A. Hensher, John M. Rose and William H.

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1 A-errors , 284 A-optimal Aadland, D ACMA (attribute aggregation in common-metric units) , 723 Adamowicz, W , 594, 818, , 954, 1072, 1073 adaptive strategies 941 additional design columns 245, 247 additional non-linearity 379 affective values 6 agents power measures (IACE) , aggregate level demand models 30 aggregate marginal effects 377 aggregation 28, , of attribute , common-metric attribute aggregation , agree non-agree model 1085 AIC (Akaike Information Criterion) 14, 203, , 553, 719 Ailawadi, K.L. 848 Akaike, H Alfnes, F , algorithms Allais, M. 907, 908 Allais paradox 913 Allenby, G.M , 1057, 1072, 1073 Allison, P alternative acceptability accounting for alternative conditioned class probabilities 115 alternative dominance 1058 alternative rejection 1058 alternative-based decision strategies alternative-based processing 939 alternative-specific constants 210, 610, 851, , 894, 949 alternative-specific parameters 49 51, , 314, 316, 317 alternative-wise transition alternatives in choice 12 irrelevant see IIA labeled 13 model results summary mutually exclusive 32 no choice alternatives 53 54, 67, partitioning 846 real market reference refining list of , 201 SC (stated choice) 969 strictly best attributes 1036 in travel choice scenario 853 Alvarez Daziano, R. 928, 930, 933 ANA (attribute non-attendance) , 723 Anderson, D.A Anderson, S. 73 ANOVA (analysis of variance) models 248 antithetic sequences APS see attribute processing strategy AR (accept reject) simulator AR (attribute rank) arbitrary non-linear function 900, 992 arc elasticities 14 Arendt, J. 780 Arentze, T. 818 Aribarg, A Armstrong, P.M. 883 Arora, N. 1072, 1073 artificial tree structure ASC (alternative-specific constants) 51 52, 53 54, 64 65, 67, 68, 82, 90 91, 441, 447, , 475, , , , 1079 Ashton, W.D. 6 7 Asmussen, S. 155 asymmetric thresholds 988 asymptotic covariance matrix 275, 777, asymptotic equivalent test see Wald statistic asymptotic standard errors , 854 asymptotic t-ratio asymptotic variance-covariance see AVC attribute addition rules 986 attribute aggregation 972, common-metric , attribute ambiguity attribute change, and consumer surplus attribute heterogeneity 723 attribute inclusion/exclusion , in this web service

2 1164 attribute levels 192, , 206, 238, 267, , 284, , 961 balance 307 choice experiment 994 design 998 effect expanded alternatives 208, 828 labels 199, , 206 LCM (latent class models) and parameter estimates 248 in pivot design 256 range effect ranges 273 reduction in stated choice 256 in stated choice experiment 549 survey design 282 attribute mean and standard deviation model summary attribute non-attendance model , , attribute package levels 934 attribute preservation/non-preservation attribute processing 15, 120, 658, 724, 819, 874, 1012 dimensional versus holistic multiple heuristics role in attribute processing heuristics, through non-linear processing 968, attribute processing strategy 874, 983, 986, 1010 attribute profiles 821 attribute range CE influence on WTP and heterogeneity and MWTP 890 profile in choice experiment 1014 attribute reduction strategy 825 attribute risk 913 attribute strategy consistency 971 attribute thresholds , accounting for responses 998 upper/lower cut-off 998 attribute transformations attribute-accumulation rule 830 attribute-based decision strategies attribute-based processing 939, 940 attribute-interaction standard deviation attribute-specific dummy variables attribute-specific standard deviation attribute-wise transition attributes 4, 12 13, 192 ACMA (attribute aggregation in commonmetric units) , 723 allocated to design columns and alternatives 208, , 828 ANA (attribute non-attendance) , 723 as blocking variable cost-related 786 design columns 239, , elemental alternatives 577 in experimental design 473 FAA (full attribute attendance) , 723 fixed attribute levels 305, 308, 309 hybrid alignable/non-alignable 958 ignored by respondents 826, 828 influences on inter-attribute correlation narrow attribute range 827 of non-chosen alternatives 887 non-considered attributes 1057 non-linear non-random parameters observable attributes and individual behavior observed 360 pivoted 786 predefined 282 in public transport alternatives 853 reference dependency refining list of relative attribute levels relevancee 887 SC (stated choice) 969, 1080 single attribute utility statistical significance of attributes and mod-specific constants model results summary Auger, P. 259 Australian case study commuter service packages 968, ordered choice model stated choice experiment Australian cities example, and bivariate probit model Australian empirical evidence results summary 881 automobile purchases, case study data automobile purchases, case study results AVC (asymptotic variance-covariance) matrix 248, , 257, 258, , , , , 316, , 1081 Average Partial Effect 14, , 754 averaging 28 in this web service

3 1165 B-estimate 284 Backhaus, K balanced designs 238 Balcombe, K , 690 bandwidth parameter BART 9 base numbers for primes conversion 139 base values to decimals conversion 141 Bateman, I.J. 959, 1010, 1072 Bates, J. 907, 913 Batley, R. 906, 907 Bayes information criteria test see BIC Bayes modeling, hierarchical Bayesian determination 409 Bayesian efficient design 254, 259, 276, , 786 Bayesian MCMC applications 321 Bayesian priors 317 Beck, M. 264, , 993, , 997 Becker, G Beharry, N. 941, 1072 behavioral aspects 193 behavioral outputs of IACE framework 1075 behavioral realism 921 behavioral rules 13 behavioral variability Ben-Akiva, M.E. 9, 10, 89 90, 104, 660, 838, benefit segments 650 Bentham, J Bernoulli, D Berry, S. 743 best worst case examples best worst data analysis 263 best-worst designs in NGene syntax best-worst experiment 290 Bettman, J.R. 943, between alternative error structure between choice set dependence BFGS algorithms 186, 559 Bhat, C. 136, 605, 707, 804, , 810, 846, 848, 851 BHHH algorithm BHHH estimators 334, 752 BIC (Bayes information criteria) test 14, 203, 710, 861, 864, 1031, 1037, 1044 Bickel, P.J. 58 Bierlaire, M. 101 binary choice model 14 15, , 345, 364 AGE coefficient 757, analysis statistics 758 application BHHH estimator 752, 854 bivariate probit models chooser characteristics 743 cluster correction consumer choice, and maximum utility consumer preferences, and random utility 743 correlated random effects data 124 data collection approach essential assumptions estimation of fit measures fixed effects functional form aspects GSOEP (German Socioeconomic Panel data) analysis , 767 Hermite quadrature 771 heterogeneity and conventional estimation inference-hypothesis tests linear utility functions Monte Carlo simulation 771 non-linear utility functions 743, 748 normalization odds ratios , 759 with panel data parameter heterogeneity parametric random utility function partial/marginal effects , random effects 768, recursive bivariate probit model , robust estimators 752 sample selection model 783 scaling effects between logit/probit coefficients semiparametric random utility function simulations simultaneous equations stochastic specification, of random utility theoretical estimators 752 binary logit models 350, 516, , 1090 binary regret binomial probit 6 bivariate probit models 40, estimated 778 models partial effects 779 partial effects and scenarios recursive bivariate probit model , estimated 781 partial effects decomposition 782 in this web service

4 1166 bivariate probit models (cont.) recursive simultaneous 790, 791 referendum voting results summary 801 Black, I Blackburn, M. 876 Blanchard, O. 912 Bliemer, M.C.J. 178, 184, 190, 191, 206, 257, 264, 266, 267, 287, 289, 301, 310, , 319, , 838, 874, 916, 994, 1014 blocking , 229 blocking strategy 271 blocking variables 240, 241, 265 Blumenschein, K Boes, S. 805, 807, 810, Bolduc, D. 928, 930, 933 bootstrapping 14, 203, , 1044 boundary values 305 Box, G.E.P. 58 Box Cox transformation 58, 59 Bradley, M. 10, 838, 963 Bradley, R.A. 6 7 brand extensions 838 Brant, R. 808 Brant test 808, 809 Bratle, P. 140 Breffle, W.S. 99, Brewer, A , 807, 1072, 1073 Bricka, S. 963 Briesch, R.A. 126, 960 Brown, T.C Brownstone, D. 109, 345, 660, 661, 848, 851, , , 879, 880, , , 887, 891, 892, 893, 896 BTL (Bradley Terry Luce) model 6 7 budget constraint Bunch, D. 248, 265, , 319 Burgess, L. 190, Burnett, N. 779 Caflisch, R.E. 140, 155 cale command 607 calibration constants 207 Camerer, C. 912 Cameron, A. 766 Cameron, T.A , 941, 1010 Campbell, D. 718, 723, 736, candidate rules 1010, Cantillo, V , 941, 950, 953, 962 CAPI (Computer Assisted Personal Interviews) 257, 431, , 715, 820, 874, , 969, 1015 Caplan, A.J CARA (constant absolute risk aversion) 912 cardinal measurement 18, 19 cardinal utility Carlsson, F. 265, , , Carp, F.M. 473 Carson, R.T. 875 Case V model 7 case study I: attribute processing heuristics, through non-linear processing 968, common-metric attribute aggression latent class specification: non-attendance and dual processing of common-metric attributes in choice analysis marginal willingness to pay/vtts self-stated processing response for commonmetric addition case study II: accounting for response certainty, acceptability of alternatives and attribute thresholds choice experiment and survey process empirical results influence of choice response certainty, alternative acceptability, and attribute case study III: choice scenarios data setting derivative willingness to pay dimensional versus holistic processing strategies influence of relative attribute levels influences of non-trading interrogation of responses to stated choice experiments: is there sense in what respondents tell us? investigating candidate evidential rules pairwise alternative plausible choice test and dominance revised model for future stated choice model estimation revision of reference alternative as value learning case-based decision theory 819 Castelar, S. 848, 851 categorically coded data, and marginal effects 376 CDF (cumulative density function) 38 39, CE-based VTTS empirical evidence results summary 882 CE (choice experiments) 14, 202, alternatives 892 attribute levels 994 attribute range profile in 1014 data in this web service

5 1167 design conditions evidence non-market impacts of public policies non-pivot designs 880 numerator/denominator ratios 890 in pivot designs , 894 studies hypothetical bias in , 879, 893, 894, and RP studies survey process CE (choice experiments) scenario, responses from one respondent CE screen 888 centipede approach/commands , 657 certainty scale 895, 896, CFA (confirmatory factor analysis) Chamberlain approach 813 Chamberlain estimator Chamberlain, G Charles River Associates 8 9 cheap talk , Chiappori, P.A Chinese restaurant study 879 Chiuri, M.C CHL see covariance heterogeneity logit choice between choice set dependence conditional 25, and demand 25, discrete see discrete choice models elasticities of no choice alternatives 53 54, 67, plausible choice test , , stated choice see SC strategic misrepresentation 959 unconditional 25, 662 choice analysis, common-metric attributes choice certainty weighted mixed logit model (model 4 in case study II) , choice certainty weighted MNL (model 2 in case study II) , choice complexity 942, choice constraints 27 29, 895 choice data modeling choice distribution in application sample 650 choice goals framework 943 choice model results choice models applications 745 behavioral considerations complexity 954 embedding decision processes with fuzzy constraints two-stage models estimation 262, history 6 11 interactions specification 836 choice preferences choice probabilities 84 86, , 515 AR (accept reject) simulator GHK simulator and marginal effects 376 in MNL model smoothed AR (accept reject) simulator unconditional 662 without closed analytical form choice reduction strategies choice response certainty and choice sequence 1016 and referencing 1051 choice scenario completion time influences 1018 responses choice sequence, and choice response 1016 choice set 4, 820, choice set generation 4 choice set heuristics choice situation choice probabilities 115 choice situations choice studies, future proposals choice task complexity 945 choice task response latencies 1018 choice tasks effect choice treatment combination 205 choice types 12 Cholesky decomposition 127, 159, , 816 Cholesky factorisation 158, 160, , Cholesky matrix 106, 159, , , , 637, 639, , 690, 698 Cholesky square root 991, 992 Cholesky transformation 158, 168 Chorus, C.G , 1012 Cirillo, C. 382 class assignment probabilities 114, 115 CNL (cross-nested logit) model 83 co-branding 838 coding, see also dummy coding; effects coding coding attributes coding schemes cognitive effort, and decision strategies cognitive load 938 Cohen, E. 259 coherency in this web service

6 1168 cojoint design methodology Collins, A.T. 259, , 959, 960, 987, 1059 column based algorithm combining data sources case study choice sets differences data enrichment data source vector of attributes 841 hypothetical bias IID assumption 841 market constraints 836, 838 MWTP (marginal willingness to pay) and hypothetical bias , see also MWTP nested logit trick no choice alternatives 869 parameter vectors equality personal constraints 836, 838 product sets 836, 838 scale parameter behavior 842 SP-RP 14 15, 409, , , superior aspects of SP/RP technological relationships 836, 837 TWTP (total willingness to pay) and hypothetical bias , see also TWTP utilities/scales 842 see also RP (revealed preference) data; SP (stated preference) data comfort 197 command line spelling errors 443 common decision processes common-metric , common-metric addition, self-stated processing response commuter mode share population weights 854 comparative judgement law 941 complexity 954 componential contextual model 1060 compromise effect 955 conditional choice 25, conditional confidence limits, random parameters conditional density 360 conditional distributions conditional estimates matrix conditional logit model 8 9 conditional parameter estimates 614 individual-specific 644, 645, individual-specific behavioral outputs individual-specific elasticities matrix 647 conditional probability 777 confidence intervals bootstrapping confirmatory with covariates factor analysis congested time framing effect conjoint analysis 203 conjunctive screening model 953 Connolly, T. 364 consequentialism effect consideration sets 988 consistent processing 939 constant shares assumption see IIA rule constant variance models 82 constrained designs constrained distribution 622, 626, 922 triangular , 658, consumer surplus, and attribute change contextual concavity model continuous variables conventional estimation 334, 769 convergence matrix convex-concave value function 908 Cook, A Cooper, B. 68 coping strategies 942 cordon-based charging Corfman, K.P Coricelli, G. 364 correlated choice sets correlated random effects correlated random parameters correlation between variables 211 and drawing from densities with main effects columns 234 and nested logit model correlation coefficient 234 correlation matrices 231, , 243, 245, 247, 430, 493, 639 cost elasticities 701 cost parameter 917 covariance heterogeneity logit model 105 covariance heterogeneity model 101 covariance matrix 13 14, 46, 89 90, 91, , 181, 275, 430, 992 covariance matrix estimator 335, 337 covariance nested logic covariance share error components covariance structures non-linearity implications 352 covariate parameters covariates and design 255, 258 respondent-specific 114 CovHet (covariance heterogeneity) model Cox, D.R. 58 CPT (Cumulative Prospect Theory) 15, 908 cross-elasticity , 375 cross-marginal effects in this web service

7 1169 cross-sectional discrete choice model 83 cross sectional error components cross-sectional MMNL models , 131, ;crosstab command CRRA (constant relative risk aversion) 909, 912 CSV (comma delimited file) 510 Cummings, R.G. 875, , cumulative distribution function cut-off 948 CV (contingent value) data CV (contingent value) evidence CV (contingent value) studies, and hypothetical bias , 879, 893 D-efficient design , , , , 715, 888, 994 end-point 274 locally optimal 306 D-errors , , 254, 268, 269, , 274, 279, 284, , 309, 310, D-optimal , , 269 D-optimal designs D-optimality plan 820 Daly, A. 9, 10, 178, 184, 594, 704, 838, , 930, 932, 933 data coding fusion RP data 320, , 472 SP data , 472, 527 weighting see also combining data sources; Nlogit data collection approach data enrichment , , 843 data pooling 1009 data source attributes 841 data-specific scale differences 865 Day, B. 959, 1010 Daykin, A. 807 Debreu, G. 7 decay function 961 decision making, by groups 15 decision process heterogeneity decision process inference, from observed choice outcomes 952 decision strategies accuracy classic 944 common decision processes group decision making household economics 1072 two-stage processing typology data 939 decision weighting function 912 degrees of freedom , Dellaert, B.C.G , 1072 delta method 14, 203, demand change in demand 27 change in quantity demand 26 and choice 25, level of demand 26 demand curve demand function denominator estimates 618 densities , dependence, between-choice set dependent variables 32 39, 45 derivative willingness to pay descriptive statistics costs and time by segment 918 socioeconomic statistics 918 ;descriptives command DeShazo, J.R. 101, 959, 1010, design codes 204, 311 design columns 239, , 245, design complexity 819 design correlation 236 design dimensions design foldover design issues, and dominance 1025 design levels 248 designs D-efficient design , , , 274, , 306, 715, 888, 994 attribute levels 998 attribute profiles 821 attributes 238, best worst designs with covariates 255, 258, experimental see experimental designs pivot designs 255, , , , 969 pivoted from reference alternative 257 S-efficient designs 264, , , 274, , statements in best-worst design sub-design attributes 822 DFP algorithms 186 Diamond, P. 875 dichotomous choice 874 Diecidue, E. 908 Diederich 938 Diff Con (Differentiation and Consolidation) theory 941 digit types 139 dimensional processing strategies in this web service

8 1170 diminishing sensitivity 908, 911 Ding, M. 879 direct elasticities , 375, 670, 698, 701 contrasts 552 disaggregate level data 31 disaggregation of decision process 941 discrete choice data 303, unlabeled discrete choice models 30 32, 34, 47, 80 and ASCs , 475, attitudes in 929 attribute transformations between-choice set dependence contextual effects covariate information covariate parameters cross-sectional 83 drawing from densities interaction effects interaction terms 479 likelihood function 118 MNL see MNL non-linear attributes 57 71, 448 non-linear parameter utility specifications observed variables panel 83 parameter estimates 265 properties respondent characteristics taste heterogeneity 11, 75 variables in see also unlabeled choice data; WTP distribution constrained , 658, , 922 triangular , 658, , 704 and heterogeneity disutility 46 Doksum, K.A. 58 Domencich, T. 8 9 dominance, role in plausible choice test , 1058 Dosher, B.A Dosman, D. 1072, 1073 Draper, N.R. 58 drawing from densities , Dstats (descriptive output) dummy coding 213 correlation comparisons 70 estimating parameters 240 example data 73 marginal utilities 65 non-linear effects rescaling 66 with status quo alternative dummy coding schemes dummy variables 284 Eagle, T. 672 EBA (elimination-by-aspects) heuristic , , 946, 950, , , 1012, EC (error components) logit 11, EC (error components) model econometric model 345 Econometric Software (Nlogit) 387 editing strategies 942 EEUT (extended EUT) MMNL in 919 effects coded design 243 effects coded variables , 308, 309 effects coding and ASC 65 correlation comparisons 70 estimating parameters 240 example data 73 marginal utilities 65 non-linear effects rescaling 66 with status quo alternative effects coding design 242 effects coding formats 215 effects coding schemes effects coding structure 215 efficient designs 223, , , 306 generation of effort accuracy trade-off 943 Einhorn, H.J. 909 EIPs (elementary information processes) elasticities 14, arc elasticity of choice , cost elasticities 701 cross-elasticities , 375, , , of demand 375 direct elasticities , 375, , , 552, , 698, 701, , 1007 dummy variables estimates, statistical significance of individual-specific key results and marginal effects 375 mean calculation mean direct results summary 1007 mean estimates in NL model point elasticity in this web service

9 1171 semi-elasticities 765 summary of 802 using ;simulation elemental alternatives attributes 577 Ellsberg, D. 907 Elrod, T. 930 Eluru, N. 804 end-point designs 208, 274 endogeneity 14, , 907 of soft variables endogeneity bias endogenous weighting entropy, as proxy for complexity 954 EQW (equal-weight) decision strategy , 944, error components in ML models ML model estimation findings 883 estimated bivariate probit model 778 estimated distribution 618 estimated parameter trade-offs estimation estimation algorithms estimator instability estimators BHHH estimators 334, 752 robust estimators 752 theoretical estimators 752 Euler Mascheroni constant 94 EUT (Expected Utility Theory) incorporating perceptual conditioning exogenous weighting , 527, 854, 907, 990 expected time parameter 917 expected utility theory 15 experiment 191 experimental design choice reduction strategies choice sets 969 considerations core objectives 223 data degrees of freedom , design blocking full factorial design generating designs labeled/unlabeled experiments levels reduction strategy problem definition refinement refining list of alternatives , 201 refining list of attributes size reduction stimuli refinement experimental design theory , marketing literature exponentials 460 extensive margin 8 9 extra design columns 246 Extreme value distribution 991 extremeness aversion heuristic , FAA (full attribute attendance) , 723 factor levels 191 factors 191 Fader, P.S. 71 Fang, K.-T. 137 ;fen command 602, 604 Fermo, G. 101 Ferrini, S. 289, 316 FF (free-flow) time design attribute FFT (free-flow travel time) 381 Fiebig, D.G. 73, 99, 110, , , 991, 1009 FIML (full information maximum likelihood) estimators , 844 finite mixture models 810 Fischer, S. 912 Fisher Information matrices , ;fisher property fixed attribute levels 305, 308, 309, 314, 316, 317 fixed effects fixed parameter mixed logit Flynn, T.N. 259 foldovers , 247 Fosgerau, M. 101, , 126, four-outcome structure 850 Fowkes, A.S. 248, Fox, C. 876, 911, 912 fractional factorial designs 208, 244, orthogonal coding 229 free-flow time framing effect 827, Frykblom, P. 874 Fuji, S , 987 full factorial design 14, coding 14, 203 full relevance group 1036 functions, variances of Galanti, S. 150 Gallet, G , 875, 876, 879 Garling, T. 895, 987 Garrod, G.D. 265 generalized mixed logit 14 15, scale and taste heterogeneity generalized mixed logit model see GMX generalized nested logit generalized multinomial logit model 673 in this web service

10 1172 Generalized Ordered logit models , 823, 824 marginal effects 826 Generalized Ordered probit model , 811 generating efficient designs generic parameter estimates 504 generic parameters 49 51, , , 308, 309, , 314, 316, 317, , estimates genetic algorithms 254 GEV (generalized Extreme value) distribution types 93 98, 848 Geweke, J. 170 GHK simulator 93, Gilboa, I. 886 Gilbride, T.J Gilovich, T. 818, 822, 823, 942 Glynn, P.W. 155 GMM (generalized method of moments) method GMNL (generalized multinomial logit model) 166 GMX (generalized mixed logit model) , , 861 direct elasticity mean estimates 698 model 1: utility space: RPL unconstrained distributions and correlated attributes , 690, 694, 696, 697, model 2: WTP Space: unconstrained distributions and correlated attributes , 690, 694, 696, 697, model 3: U-Specification: GMX unconstrained T s with scale and taste heterogeneity and correlated attributes , 690, 694, 696, 697, model 4: RPL t,1 688, 697 model results summary Nlogit syntax in utility space variance parameters 698 GMXL (generalized random parameter/mixed logit model) 697, GOCM (generalized ordered choice model) , , Nlogit commands Goldstein, W.M. 909 Golob, T. 928 Gonzales, R. 941 good deal/bad deal heuristic 959, 960 goodness of fit 702, 792, 888, , 986 Goodwin, P. 798, Goos, P Gourville, J.T gradient matrices Greene, W.H. 14, 73, 103, 110, , 353, 360, 622, , , 677, 694, 707, 708, 718, 736, 751, 768, , 777, 779, 780, 804, 805, 807, 808, 810, , 813, 850, , , , 901, 941, , , 1009 group decision making group equilibrium model results 1088 group equilibrium preferences , GSOEP (German Socioeconomic Panel data) analysis , 767 Gumbel scale MNL 993 Haab, T.C. 352 Haaijer, R habit persistence 961 Hajivassiliou, V. 170 ;halton command Halton draws 277, 606, Halton, J. 136 Halton sequences , 157, 168, 254, 606, correlation structure 164 SHS (shuffled Halton sequences) , Hanemann, M. 875 Harrison, G , 874, 875, , 879, , 905, 911, 912 Hausman, J. 875 HCM (Hybrid Choice Models) data arrangements 935 latent attitude variables 928, 931 likelihood function 932 main elements of multinomial choice utility functions 932 observed indicators 932 overview of socio-demographic characteristics underlying perceptions/attitudes 928 health care utilisation cross-tabulation 776 Heckman, J. 707, 719, 782 Hensher, D.A. 10, 14, 26 27, 30 31, 73, 74, 76, 99, , 103, 110, , 190, 191, 207, 259, 264, 265, , 275, , 360, , , 571, 594, 622, , , 677, 694, 718, 719, 736, 773, 774, 787, 788, 793, 798, , 808, , , 822, 838, 846, 848, 850, 853, , , 874, , , 887, , , 894, 901, 913, 914, 917, 921, 938, 940, 941, 942, 945, 947, , , 959, 960, 962, , 969, , 1009, 1010, 1012, 1051, 1059, 1072, 1073, 1087 in this web service

11 1173 Hermite quadrature 771 Hess, S. 73, 99, 136, 148, 381, , 719, 723, 724, 736, , 886, 941, 1010, 1059 Hessian matrix , heterogeneity additional unobserved effects 669 attribute heterogeneity 723 behavioral processes 724 and conventional estimation decision process heterogeneity distributions in kernel logit model 660 latent heterogeneity and individual behavior in mean of random parameters , , 646 in mean of selective random parameters parameter heterogeneity preference heterogeneity 11, 99, 120, 665, , 672, 723 in preference parameters 992 process heterogeneity 938, 981 random parameter standard deviations 669 scale heterogeneity 11, 99, 110, 120, , , , , 865, 991, 992 taste heterogeneity 11, 75, unobserved preference heterogeneity in variances 618, WTP estimates 654 heteroskedastic MNL (model 5 in case study II) , heteroskedastic MNL with scale heterogeneity (model 6 in case study II) , heteroskedastic ordered probit model 812 heteroskedasticity , 335, , 809, 989, 991 in ordered choice model 810 temporal 961 in variances , , 646, 664 heuristics MCD heuristic , 951, 952, 966, attribute processing heuristics through nonlinear processing 968, and biases choice set heuristics decision strategies , dimensional versus holistic attribute processing EBA heuristic , , 946, 950, 951, , 1012, elimination-by-aspects heuristic 1012 extremeness aversion heuristic , good deal/bad deal heuristic 959, 960 identification of 937 imposition of threshold 870 just-noticeable difference heuristic , and latent class models LEX heuristic , 945, , , literature 822 mixed heuristic model multiple heuristics in attribute processing non-trading influence outcome heuristics and pairwise alternative plausibility process heuristics 15, , , 1059 RAM heuristics reference alternative as value learning relational heuristics relative attribute levels specific attribute processing heuristics 1009 and stated choice experiments 1010 in utility function 1059 value learning heuristic 960, , HEV (heteroskedastic Extreme value) model 83, 846 HG-SMNL (heteroskedastic Gumbel scale MNL) model (model 6 in case study II) , Hickernell, F.J hierarchical Bayes modeling HMNL (heteroskedastic MNL) model model 5 in case study II , Ho, T. 912 Hole, A.R. 736 holistic processing strategies Hollander, Y. 907 Holm, A. 780 Holmes, C. 473 Holt, C.A. 908, 912 homoskedastic linear regression model 303 homoskedasticity assumption 810 household economics 1072, 1085 Huber, J. 248, 253, 265, , 467 Hull, C.L. 6 hybrid alignable/non-alignable attributes 958 hybrid coding schemes 68 hypothesis tests hypothetical bias in CV studies , 879, 893 in CE studies , 879, 893, 894, future study proposals response certainty in RP-CE deviations hypothetical yes in this web service

12 1174 IACE (Interactive Agency Choice Experiments) agent preference model system (stages 1 3) agent-specific models 1076, agents power measures , agents power play ASC-free parameters 1079 automobile purchases case study data automobile purchases case study results behavioral outputs 1075 group equilibrium preferences on internet 1079 Nlogit commands passes rounds IASs (Internet Aided Surveys) 431 Ibáñez, N. 906 IIA (Irrelevant Alternatives) 7 assumption , 330, 331, 516, property 101 rule 6 7 testing 457 IID (Independence of Identically Distributed) assumption 205, 373, 374, , 841, 844, 900 illustrative Australian empirical evidence results summary 881 illustrative stated choice screen 1081 imperfect discrimination 6 imposition of threshold heuristics 870 in-sample prediction success 1000 incentive-aligned approach 879 incidental parameters problem independent distributions 127 independent variables indifference curves 20 individual behavior, and observable attributes/ latent heterogeneity individual preferences see preferences individual-specific elasticities individual-specific marginal utilities individual-specific parameter estimates: conditional parameters individual-specific parameter vector 849 individual-specific thresholds 950 individual s demand function inertia 1029 inference-hypothesis tests influence of choice response certainty, alternative acceptability, and attribute thresholds information acquisition monitoring information matrix insignificant alternatives 196 insignificant parameter estimates 615 instrument calibration intelligent draws intensive margin 8 9 inter-attribute correlation interaction columns , interaction effect 59 60, , , , interaction effect parameter 60 interaction terms 479, 482, 626, 629 internal market analysis 930 IRDA (Irrelevance of Regret-Dominated Alternatives) 366 Isacsson, G , , , 879, , 891, 892, 893 ISDA (Irrelevance of Statewise Dominated Alternatives) principle 364, 365, 366 Islam, T. 259 Ison, S. 796 ITS (Institute of Transport Studies) 466 IV (inclusive value) 103 IV (inclusive value) parameters 324, , , IV (inclusive value) start values 567 jackknife correction approach 382 Johannesson, M. 876, 896, 987, 988, 1007 Johansson-Stenman, O John, J.A. 58 Johnson, R. 313 joint probability 777 Jones, P. 76, 946 Judd, C.M Jung, A. 150 just-noticeable difference heuristic , K & R (Krinsky and Robb) method Kahneman, D. 364, 381, 874, 886, 887, 908, 909, 911, 912, Kanninen, B.J. 248, , 1081 Kaye-Blake, W.H Keane, M.P. 110, 170, 930 Keppel, G. 310 kernel density estimator 619 kernel estimator, draws sample 358 kernel logit model 106, 660 kernel weighting function 620 Kessels, R. 267, 276, 306, key data descriptive overview King, D. 796, 813 Kivetz, R. 955, in this web service

13 1175 Klein, R. 126, 745 KLIC (Kullback Leibler Information Criterion) , 553 Knight, F.H. 906, 907 knowledge base 256 Knowles 1073, 1087 known bias function 876 KR (Krinsky Robb) test 14, 203, , 754 Kramer, J. 796 Krinsky, I. 346, 1044 Kuehl, R.O. 223 Kuhfeld, W.F. 303, labeled alternatives 13, labeled choice data set 437, labeled choice experiment 205, 993 labeled experiments Ladenburg, J lagged response formulation 382 Lampietti, J Lancaster, K.J. 57 Lancsar, E , 1028 Landry, C.E latency data 966 latent attitude variables 928, 931 latent attributes 930 latent class models 810, , , , , 984, 986 MCD role 1045 equality constrained 960 latent class specification latent heterogeneity, and individual behavior latent variables 929, 930 measurement equations 933 Laury, S.K. 908, 912 Lave, Charles 8 law of comparative judgement 6 Layton, D , 874, , 941, 969, LCM (latent class models) 13, 14 15, , D-efficiency design 715 attribute levels attribute non-attendance model CAPI (Computer Assisted Personal Interviews) 715 case study case study results class allocation Gumbel error component 734 and heterogeneity , 711, and MNL model 706 Model 1 (fixed parameters, no ANA, no ACMA) , 725 Model 2 (fixed parameters, FAA, ANA, ACMA) , Model 3 (random parameters, no ANA, no ACMA) , 729 Model 4 (random parameters, FAA, ANA, ACMA) , in Nlogit random parameter LC model , scale-adjusted standard LC model WTP estimates 713, 721, 722 Lehmann, D.R Leong, W. 988 Lerman, S.R. 104, 167 level balance 251 level of satisfaction see satisfaction level of utility see utility levels reduction strategy Levinsohn, J. 743 Levinson, D. 796 LEX (lexicographic choice) decision strategy , 944, 945, , , , Li, Z. 913, 962 likelihood estimation likelihood function 13, in HCM 932 limit cards , 880, 895, LIML (limited information maximum likelihood) estimators Lindzey, G. 473 line extensions 838 linear additive utility expression 937 linear estimates 216 linear regression analysis linear regression models 32 39, 78, 80, 86, 248, , 303, linear regression results 35 linear utility function 49, 80 linear-additive utility function 905 link functions 37 38, 45, 76 Lisco, Thomas 8 LISREL software 928 List, J.A , 875, LL (log-likelihood) estimation 122 cross-sectional model 130 panel model 132 using count data 122 using proportion data 122 LL (log-likelihood) function 13, , , , , at convergence 610 simulated , 134 LL (log-likelihood) ratio test 452 in this web service

14 1176 LM (Lagrange multiplier) statistic 326 LM (Lagrange multiplier) test 14, 203, locally optimal designs , 306, 309 locally optimal prior parameter estimates 309, 312 log-odds 43 logit models 42 44, 79, 80, 81, 345, 745 add on insurance take up 759 based on multivariate Extreme value distribution and discrete choice data 248 estimated partial effects 764 generalized mixed logit 14 15, generalized nested logit Generalized Ordered logit model kernel logit 106 MMNL (mixed multinomial logit) model see MMNL MNL (multinomial logit) see MNL multinomial choice NL (nested logit) see NL (nested logit) model non-linear 314 orthogonal designs probabilities 133, 663 results 44 SMNL (scaled multinomial logit) 111 logit response function 7 logit versus probit 98 lognormal distribution 112, 622 loss aversion 908, Louviere, J.J. 10, 30, 71, 99, , 190, 191, 208, 257, 259, 264, , , , , 371, 372, 373, , 504, , 571, 672, 836, 838, , , 1009, 1028 LPLA (linear in parameters and linear in attributes) 351, LR (likelihood ratio) statistic 326 LR (likelihood ratio) test 14, 203, Luce, M.F. 907, 938 Luce, R.D. 6 7 Lusk, J.L. 303, , 874, McClelland, G.H McConnell, K.E. 352 McElvey, W. 805 McFadden, D. 6, 8 9, 10, 169, 170, , 594, 846, 901, 905, 937, 992 McFadden Pseudo R-squared 572 McNair, B. 718, , 959, 960, 963, 1010, , 1059 Maddala, T. 780 Magic Ps 305, 318 main effect parameter 59 main effects , 218, main effects design , Manheim, Marvin 9 10 Manly, B.F. 58 MANOVA model Manski, C.F. 167, 946 marginal density 13 marginal effects ordered/generalized ordered logit models 826 marginal probability 777 marginal utilities 49, 98, 198, 199 marginal/partial effects 14, , marital status takeup, partial effect on 765 Marley, A. 259 Marschak, J. 7 Martinsson, P. 265, , , Matas, A. 907 mathematical probability 74 maximum simulated likelihood , 675 MCD (majority of confirming dimensions) heuristic , 944, 951, 952, , alternative to 966 identifying role (latent class model) 1045 influence of ME (multiplicative errors) model mean threshold parameters 825 memory-based judgements Microsoft Excel program , 163, 231, 234, 415 Million, A. 756 MIMIC (Multiple Indicator Multiple Cause) model minimum treatment combinations 218 minimum-regret calculus 1012 minimum-regret theory 819 mixed heuristic model mixed logit (model 3) mixed logit (model 4) mixed logit models see ML mixed pdf random utility model 949, 966 ML (mixed logit) model , , alternative-specific constants 610 asymmetric distribution 604 basic commands choice-based weights 860 conditional individual specific matrix mean random parameter estimates , standard deviation random parameter estimates , willingness to pay estimates conditional parameter estimates 614 in this web service

15 1177 constrained distribution direct elasticities 670 elasticities empirical results summary error components in , ;fen command 602, 604 fixed/non-random parameter terms 602 generalized see generalized mixed logit ;halton command individual-specific parameter estimates: conditional parameters interpreting with Nlogit and log-likelihood ratio 612 and MNL models model 2, mixed logit with unconstrained distributions model 3, restricting sign and range of a random parameter model 3 in case study II , , model 4, heterogeneity in mean of random parameters model 5, heterogeneity in mean of selective random parameters model 6, heteroskedasticity/heterogeneity in variances model 7, allowing for correlated random parameters model results summary , models ML no choice-based weights 859 panel specification parameter estimates preference heterogeneity 855 random parameter conditional confidence limits random parameter estimates , random seed generator results summary ;rpl command 602 SHS (shuffled Halton sequences) , shuffled uniform vectors unconditional parameter estimates 614, WTP (willingness to pay) 652 ML (mixed logit) models MLE (maximum likelihood estimation) 13 14, 86, , 445 and fixed effects MLHS (Modified Latin Hypercube Sampling) 136, , 606 MMNL (mixed multinomial logit) model 11, 13 15, 95 96, 99, , , 129, 133, 166, , 266, 309, , 382, in EEUT framework 919 with non-linear utility functions standard deviation around WTP estimate 922 see also panel MMNL models MNL (multinomial logit) model 10, 13, 14 15, 95 96, , , , 116, , 203, 272, , , 287, 305, 308, , 316, , , 376, bad data base comparison models choice model comparison choice model interactions choice probabilities discrete choice models properties indirect utility functions interpreting output LL (log-likelihood) function , , , MLE (maximum likelihood estimation) 13 14, 86, , 445 MNL command in Nlogit model 1 in case study II , model convergence iterations model fit determination and NL (nested logit) models options/features 664 overall model significance sample size determination 445 ;show command starting values 610 weighting criteria 445 see also Nlogit model applications, using common data set up model averaging process model calibration model convergence 182 model results summary , modeling utility modified Federov algorithm , Moffitt, P. 807 Money Pump 366 Mongin, P. 905 Monte Carlo evaluation methods Monte Carlo simulation 93, 97, 258, 289, 352, 771 see also PMC; QMC Morey, E.R. 99, Morgenstern, O. 905 Morikawa, T. 10, 838, 930 Morokoff, W.J. 140 MRS (marginal rate of substitution) 378, , 1008 see also WTP in this web service

16 1178 MT (Mixed Transition) multi-attribute environment 18 multinomial choice utility functions 932 multinomial logit see MNL multinomial probit model 10, 167 multiple attribute lists 874 multiple heuristics multiple price lists 874 multivariate distribution 127, 158, multivariate draws 851 multivariate Extreme value distribution multivariate normal distribution multivariate parameter distribution 134 multivariate probit models , see also probit Mundlak approach , 773, 787, 813 Murphy, J , 876 mutually exclusive alternatives see alternatives MWTP (marginal willingness to pay) CV (contingent value) data CV (contingent value) evidence and CV (contingent value) studies 875 benchmarks of interest 880 CE (choice experiments) data , 891 CE (choice experiments) evidence and CE (choice experiments) studies and cross-section studies 886 data spectrum dichotomous choice 874 experimental focus and hypothetical bias , and individuals real market activity 882 key assumptions/approaches key influences on non-experimental focus 872 numerator/denominator effects pivot-based choice experiments 887 and real markets 872 role of numerator/denominator , 891 RP (revealed preference) data 873, 891 SC (stated choice) data and time savings travel time savings value VTTS value Nachtsheim, C.J naive pooling 373, 377, 505 narrow attribute range 827 Nelder Mead algorithm 318 nested logit trick , Netzer, O. 955, NGene program 14, 190, 191, 202, 203, 223, 242, 254, , 258, 264, 267 NGene syntax in all model types 291 best worst designs Design 1: standard choice set up Design 2: pivot design set up Design 3: D-efficient choice design Niederreiter, H. 137, 155 NL (nested logit) model 9 10, 13, 14 15, 95 96, , 116, 178, 317, IV (inclusive value) parameters equality assumption estimates insignificant 574 normalizing and constraining start value 567 IV (inclusive value) start values, specifying 567 IV (inclusive value) variable calculation 577 choice-based weights 858 command syntax , 564 and correlation covariance nested logic CovHet (covariance heterogeneity) model degenerate alternatives/branches and scale parameters 584 elasticities estimation generalized mixed logit 14 15, generalized nested logit and IIA/IID assumptions levels 564, , , log-likelihood (LL) nested logit trick , nested models tests 320, Nlogit commands no choice-based weights 858 output interpretation partial effects preference heterogeneity 855 probabilities calculation/estimation RU2 nested logit specification 567 tree structures artificial degenerate branches in Nlogit , 564 utility functions WESML (weighted estimation maximum likelihood) method 854 see also Nlogit program Nlogit program 14, 112, 191, 320, 324, 336, 337, 347, 372, 378, IV variable calculation 577 ML (mixed logit) model basic commands and ML models 601 in this web service

17 1179 ;show command aggregation method 505 basic data set up binary choice application calibration 410 choice data entered on single line choice data modeling choice probability combining data sources combining SP-RP data 409 command format command methods commands , 437, , , 466, , , 515, , 645, , , NL (nested logit) model , 600 concurrent simulations 522 conditional choice contingency table converting single line data commands correlation command 637, 639 correlation matrix 493, 639 and covariance matrix 430 CSV (comma delimited file) 510 data cleaning data entered in single line 425 data entering data entering in data editor data of interest subset 493 data melding 405 data stacking 405 data understanding data weighting default missing value 420 delay choice variable adding 413 descriptive statistics 493 ;descriptives command diagnostic messages Dstats descriptive output elasticities , , , elasticities output 492 endogenous weighting error messages exogenous variable weighting exogenous weighting 527 exogenous weights entered 411 export command 493, 507 fen parameter characters 604 FIML (full information maximum likelihood) estimators functions performed by 391 general choice data 402 IACE importing data from file importing small data set from text editor indirect utility functions initial MNL model 493 installation 388 intelligent draw methods 606 Johnson Sb distributed parameter 603 kernel densities 621 labeled choice data set 437, LCM (latent class models) leaving session 391 LHS choice variable 426 limitations in 398 LIML (limited information maximum likelihood) estimators Log-likelihood (LL) functions lognormal distributed parameter 603 marginal effects output 492 Maximize command mean centring variables 495 missing data 412, 495 MNL command MNL output model calibration model parameters model results summary 550 multiple data sets entering 405 naive pooling 373, 377, 505 NL ML estimation NL (nested logit) see NL (nested logit) NLWLR no choice alternative , no choice variable adding 413 observation removal 547 overview parameter estimates parameter names parameter names selection 494 partial output 492 partial/marginal effects 14, , , binary choice PARTIALS command 515 prediction success 492 probabilities calculation program crashes 433 project file 390, 414 Project File Box question mark (?) in commands RAM heuristics random parameter covariances 637 random parameter draws random parameters 603, Rayleigh variable 621 reading the data 388, 493 in this web service