@article{HEAD:MAYE:04,
author = {Head, Keith and Mayer, Thierry},
title = {Market Potential and the Location of Japanese Investment in
                  the European Union},
journal = {The Review of Economics and Statistics},
volume = {86},
number = {4},
pages = {959-972},
year = {2004},
doi = {10.1162/0034653043125257},
eprint = { https://doi.org/10.1162/0034653043125257 },
abstract = { This paper develops a theoretical model of location
                  choice under imperfect competition to formalize the
                  notion that firms prefer to locate “where the
                  markets are.” The profitability of a location
                  depends on a term that weights demand in all
                  locations by accessibility. Using a sample of
                  Japanese firms' choices of regions within European
                  countries, we compare the theoretically derived
                  measure of market potential with the standard form
                  used by geographers. Our results show that market
                  potential matters for location choice but cannot
                  account entirely for the tendency of firms in the
                  same industry to agglomerate. }
}

@article{LEON:MIGU:17,
Author = {León, Gianmarco and Miguel, Edward},
Title = {Risky Transportation Choices and the Value of a Statistical Life},
Journal = {American Economic Journal: Applied Economics},
Volume = {9},
Number = {1},
Year = {2017},
Month = {January},
Pages = {202-28},
DOI = {10.1257/app.20160140}}

@article{HUBE:TRAI:00,
    author = {Joel Huber and Kenneth Train},
    title = {On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths},
    journal = {Marketing Letters},
    year = {2000},
    volume = {12},
    pages = {259--269}
}

@TechReport{REVE:TRAI:01,
  author={David Revelt and Kenneth Train},
  title={{Customer-Specific Taste Parameters and Mixed Logit: Households' Choice of Electricity Supplier}},
  year=2001,
  month=Jan,
  institution={University Library of Munich, Germany},
  type={Econometrics},
  url={https://ideas.repec.org/p/wpa/wuwpem/0012001.html},
  number={0012001},
  abstract={In a discrete choice situation, information about the
                  tastes of each sampled customer is inferred from
                  estimates of the distribution of tastes in the
                  population. First, maximum likelihood procedures are
                  used to estimate the distribution of tastes in the
                  population using the pooled data for all sampled
                  customers. Then, the distribution of tastes of each
                  sampled customer is derived conditional on the
                  observed data for that customer and the estimated
                  population distribution of tastes (accounting for
                  uncertainty in the population estimates.) We apply
                  the method to data on residential customers' choice
                  among energy suppliers in conjoint-type
                  experiments. The estimated distribution of tastes
                  provides practical information that is useful for
                  suppliers in designing their offers. The
                  conditioning for individual customers is found to
                  differentiate customers effectively for marketing
                  purposes and to improve considerably the predictions
                  in new situations.},
  keywords={},
  doi={},
}



@article{HERR:KLIN:99,
author = {Herriges, Joseph A. and Kling, Catherine L.},
title = {Nonlinear Income Effects in Random Utility Models},
journal = {The Review of Economics and Statistics},
volume = {81},
number = {1},
pages = {62-72},
year = {1999},
doi = {10.1162/003465399767923827},
eprint = { https://doi.org/10.1162/003465399767923827 },
abstract = { Random utility models (RUMs) are used in the literature
                  to model consumer choices from among a discrete set
                  of alternatives, and they typically impose a
                  constant marginal utility of income on individual
                  preferences. This assumption is driven partially by
                  the difficulty of constructing welfare estimates in
                  models with nonlinear income effects. Recently,
                  McFadden (1995) developed an algorithm for computing
                  these welfare impacts using a Monte Carlo Markov
                  chain simulator for generalized extreme-value
                  variates. This paper investigates the empirical
                  consequences of nonlinear RUMs in the case of
                  sportfishing modal choice, while refining and
                  contrasting the available methods for welfare
                  estimation. }
}


@article{FOK:PAAP:VAND:12,
author = {Fok, Dennis and Paap, Richard and Van Dijk, Bram},
title = {A Rank-Ordered Logit Model With Unobserved Heterogeneity In
                  Ranking Capatibilities},
journal = {Journal of Applied Econometrics},
volume = {27},
number = {5},
pages = {831-846},
doi = {10.1002/jae.1223},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/jae.1223},
abstract = {Abstract To study preferences, respondents to a survey are
                  usually asked to select their most preferred option
                  from a set. Preferences can be estimated more
                  efficiently if respondents are asked to rank all
                  alternatives. When some respondents are unable to
                  perform the ranking task, using the complete ranking
                  may lead to a substantial bias. We introduce a model
                  which endogenously describes the ranking
                  capabilities of individuals. Estimated preferences
                  based on this model are more efficient when at least
                  some individuals are able to rank more than one
                  item, and they do not suffer from biases due to
                  ranking inabilities of respondents. Copyright 2010
                  John Wiley \& Sons, Ltd.},
year = {2012}
}

@InCollection{MCFA:73,
  Title                    = {Conditional Logit Analysis of Qualitative Choice Behaviour},
  Author                   = {D. McFadden},
  Booktitle                = {Frontiers in Econometrics},
  Publisher                = {Academic Press New York},
  Year                     = {1973},
  Address                  = {New York, NY, USA},
  Editor                   = {P. Zarembka},
  Pages                    = {105-142}
}

@article{MCFA:74,
title = "The measurement of urban travel demand",
journal = "Journal of Public Economics",
volume = "3",
number = "4",
pages = "303 - 328",
year = "1974",
issn = "0047-2727",
doi = {10.1016/0047-2727(74)90003-6},
author = "Daniel McFadden"
}

@BOOK{TRAI:09,
title = {Discrete Choice Methods with Simulation},
author = {Train, Kenneth},
year = {2009},
publisher = {Cambridge University Press},
abstract = {This book describes the new generation of discrete choice
                  methods, focusing on the many advances that are made
                  possible by simulation. Researchers use these
                  statistical methods to examine the choices that
                  consumers, households, firms, and other agents
                  make. Each of the major models is covered: logit,
                  generalized extreme value, or GEV (including nested
                  and cross-nested logits), probit, and mixed logit,
                  plus a variety of specifications that build on these
                  basics. Recent advances in Bayesian procedures are
                  explored, including the use of the
                  Metropolis-Hastings algorithm and its variant Gibbs
                  sampling. This second edition adds chapters on
                  endogeneity and expectation-maximization (EM)
                  algorithms. No other book incorporates all these
                  fields, which have arisen in the past 25 years. The
                  procedures are applicable in many fields, including
                  energy, transportation, environmental studies,
                  health, labor, and marketing.},
doi = {10.1017/CBO9780511805271}}

@BOOK{CAME:TRIV:05,
title = {Microeconometrics},
author = {Cameron, A. and Trivedi, Pravin},
year = {2005},
publisher = {Cambridge University Press},
abstract = {This book provides the most comprehensive treatment to
                  date of microeconometrics, the analysis of
                  individual-level data on the economic behavior of
                  individuals or firms using regression methods for
                  cross section and panel data. The book is oriented
                  to the practitioner. A basic understanding of the
                  linear regression model with matrix algebra is
                  assumed. The text can be used for a
                  microeconometrics course, typically a second-year
                  economics PhD course; for data-oriented applied
                  microeconometrics field courses; and as a reference
                  work for graduate students and applied researchers
                  who wish to fill in gaps in their
                  toolkit. Distinguishing features of the book include
                  emphasis on nonlinear models and robust inference,
                  simulation-based estimation, and problems of complex
                  survey data. The book makes frequent use of
                  numerical examples based on generated data to
                  illustrate the key models and methods. More
                  substantially, it systematically integrates into the
                  text empirical illustrations based on seven large
                  and exceptionally rich data sets.},
doi = {10.1017/CBO9780511811241}
}

@article{BHAT:95,
title = "A heteroscedastic extreme value model of intercity travel mode choice",
journal = "Transportation Research Part B: Methodological",
volume = "29",
number = "6",
pages = "471 - 483",
year = "1995",
issn = "0191-2615",
doi = {10.1016/0191-2615(95)00015-6},
author = "Chandra R. Bhat",
abstract = "Estimation of disaggregate mode choice models to estimate
                  the ridership share on a proposed new (or improved)
                  intercity travel service and to identify the modes
                  from which existing intercity travelers will be
                  diverted to the new or upgraded service constitutes
                  a critical part of evaluating alternative travel
                  service proposals to alleviate intercity travel
                  congestion. This paper develops a new
                  heteroscedastic extreme value model of intercity
                  mode choice that overcomes the ‘independence of
                  irrelevant alternatives’ (IIA) property of the
                  commonly used multinomial logit model. The proposed
                  model allows a more flexible cross-elasticity
                  structure among alternatives than the nested logit
                  model. It is also simple, intuitive and much less of
                  a computational burden than the multinomial probit
                  model. The paper discusses the non-IIA property of
                  the heteroscedastic extreme value model and presents
                  an efficient and accurate Gaussian quadrature
                  technique to estimate the heteroscedastic model
                  using the maximum likelihood method. The multinomial
                  logit, alternative nested logit structures, and the
                  heteroscedastic model are estimated to examine the
                  impact of improved rail service on business travel
                  in the Toronto-Montreal corridor. The nested logit
                  structures are either inconsistent with utility
                  maximization principles or are not significantly
                  better than the multinomial logit model. The
                  heteroscedastic extreme value model, however, is
                  found to be superior to the multinomial logit
                  model. The heteroscedastic model predicts smaller
                  increases in rail shares and smaller decreases in
                  non-rail shares than the multinomial logit in
                  response to rail-service improvements. It also
                  suggests a larger percentage decrease in air share
                  and a smaller percentage decrease in auto share than
                  the multinomial logit. Thus, the multinomial logit
                  model is likely to provide overly optimistic
                  projections of rail ridership and revenue, and of
                  alleviation in inter-city travel congestion in
                  general, and highway traffic congestion in
                  particular. These findings point to the limitations
                  of the multinomial logit and nested logit models in
                  studying intercity mode choice behavior and to the
                  usefulness of the heteroscedastic model proposed in
                  this paper."
}

@article{KOPP:WEN:00,
title = "The paired combinatorial logit model: properties, estimation and application",
journal = "Transportation Research Part B: Methodological",
volume = "34",
number = "2",
pages = "75 - 89",
year = "2000",
issn = "0191-2615",
doi = {10.1016/S0191-2615(99)00012-0},
author = "Frank S. Koppelman and Chieh-Hua Wen",
keywords = "Discrete choice, Extreme value models, Intercity travel, Logit, Mode choice",
abstract = "The Independence of Irrelevant Alternatives (IIA) property
                  of the multinomial logit (MNL) model imposes the
                  restriction of zero covariance between the utilities
                  of pairs of alternatives. This restriction is
                  inappropriate for many choice situations; those in
                  which some pairs or sets of alternatives share the
                  same unobserved attributes. The nested logit (NL)
                  model relaxes the zero covariance restriction of the
                  MNL model but imposes the restriction of equal
                  covariance among all alternatives in a common nest
                  and zero covariance otherwise. The paired
                  combinatorial logit (PCL) model relaxes these
                  restrictions further by allowing different
                  covariances for each pair of alternatives. This
                  relaxation enables the estimation of differential
                  competitive relationships between each pair of
                  alternatives. The closed form of the PCL model
                  retains the computational advantages of other logit
                  models while the more flexible error correlation
                  structure, compared to the MNL model and NL models,
                  enables better representation of many choice
                  situations. This paper describes the derivation,
                  structure, properties and estimation of the PCL
                  model. The empirical results demonstrate that the
                  PCL model is statistically superior to the MNL and
                  NL models and may lead to importantly different
                  travel forecasts and policy decisions."
}

@article{WEN:KOPP:01,
title = "The generalized nested logit model",
journal = "Transportation Research Part B: Methodological",
volume = "35",
number = "7",
pages = "627 - 641",
year = "2001",
issn = "0191-2615",
doi = {10.1016/S0191-2615(00)00045-X},
author = "Chieh-Hua Wen and Frank S Koppelman",
keywords = "Discrete choice, Random utility models, Travel demand, Logit, Intercity travel",
abstract = "The generalized nested logit (GNL) model is a new member
                  of the generalized extreme value family of
                  models. The GNL provides a higher degree of
                  flexibility in the estimation of substitution or
                  cross-elasticity between pairs of alternatives than
                  previously developed generalized extreme value (GEV)
                  models. The GNL model includes the paired
                  combinatorial logit (PCL) and cross-nested logit
                  (CNL) models as special cases. It also includes the
                  product differentiation (PD) model, which represents
                  the elasticity structure associated with
                  multi-dimensional choices, and the ordered
                  generalized extreme value model, which represents
                  the elasticity structure associated with ordered
                  alternatives, as special cases. The GNL model
                  includes the two-level nested logit (NL) model as a
                  special case and can approximate closely multi-level
                  nested logit models. It accommodates differential
                  cross-elasticity among pairs of alternatives through
                  the fractional allocation of each alternative to a
                  set of nests, each of which has a distinct logsum or
                  dissimilarity parameter. An empirical example of
                  intercity mode choice confirms the statistical
                  superiority of the GNL model to the paired
                  combinatorial logit, cross-nested logit and nested
                  logit models and indicates important differences in
                  cross-elasticity relationships across pairs of
                  alternatives."
}

@article{FOWL:10,
Author = {Fowlie, Meredith},
Title = {Emissions Trading, Electricity Restructuring, and Investment
                  in Pollution Abatement},
Journal = {American Economic Review},
Volume = {100},
Number = {3},
Year = {2010},
Month = {June},
Pages = {837-69},
DOI = {10.1257/aer.100.3.837}}

@TechReport{BENA:BOLD:BRAD:93,
  author={Ben-Akiva, M. and Bolduc, D. and Bradley, M.},
  title={Estimation of Travel Choice Models with Randomly Distributed Values of Time},
  year=1993,
  institution={Laval - Recherche en Energie},
  type={Papers},
  url={https://ideas.repec.org/p/fth/lavaen/9303.html},
  number={9303},
  abstract={No abstract is available for this item.},
  keywords={travel ; economic models},
}

@article{MEIJ:ROUW:06,
author = {Meijer, Erik and Rouwendal, Jan},
title = {Measuring welfare effects in models with random coefficients},
journal = {Journal of Applied Econometrics},
volume = {21},
number = {2},
pages = {227-244},
doi = {10.1002/jae.841},
abstract = {Abstract In economic research, it is often important to
                  express the marginal value of a variable in monetary
                  terms. In random coefficient models, this marginal
                  monetary value is the ratio of two random
                  coefficients and is thus random itself. In this
                  paper, we study the distribution of this ratio and
                  particularly the consequences of different
                  distributional assumptions about the
                  coefficients. It is shown that important
                  characteristics of the distribution of the marginal
                  monetary value may be sensitive to the
                  distributional assumptions about the random
                  coefficients. The median, however, is much less
                  sensitive than the mean. Copyright 2006 John Wiley
                  \& Sons, Ltd.},
year = {2006}
}

@article{DIPA:NAUF:CHIN:94,
author = { Dipak C.  Jain and Naufel J.  Vilcassim and Pradeep K.
                  Chintagunta },
title = {A Random-Coefficients Logit Brand-Choice Model Applied to
                  Panel Data},
journal = {Journal of Business \& Economic Statistics},
volume = {12},
number = {3},
pages = {317-328},
year  = {1994},
publisher = {Taylor & Francis}
}

@article{PAAP:FRAN:00,
author = {Paap, Richard and Franses, Philip Hans},
title = {A dynamic multinomial probit model for brand choice with
                  different long-run and short-run effects of
                  marketing-mix variables},
journal = {Journal of Applied Econometrics},
volume = {15},
number = {6},
pages = {717-744},
abstract = {Abstract In this paper we propose a dynamic multinomial
                  probit model in order to estimate the long-run and
                  short- run effects of marketing mix variables on
                  brand choice. The latent variables, which contain
                  the unobserved perceived utilities, follow a
                  first-order vector error correction autoregressive
                  process of order 1 with current and lagged
                  explanatory variables. The unrestricted
                  autoregressive parameter matrix concerns the
                  intertemporal correlation in perceived utilities of
                  households over purchase occasions and indicates the
                  persistence in brand choice. As explanatory
                  variables we consider relative prices and
                  promotional activities like feature and display. An
                  important and novel feature of our model is that it
                  allows for different long-run and short-run effects
                  of promotional activities, thereby extending the
                  models that are currently available in the
                  literature. Additionally, to account for different
                  base preferences for brands across households, we
                  allow for consumer heterogeneity. Our application
                  concerns a panel of households choosing among
                  several brands of a FMCG. Our estimated model turns
                  out to be an improvement over a static model and
                  over a model with only short-run effects, in terms
                  of in-sample fit and out-of-sample
                  forecasts. Copyright 2000 John Wiley \& Sons, Ltd.},
year = {2000}
}

@article{MCFA:TRAI:00,
 ISSN = {08837252, 10991255},
 abstract = {This paper considers mixed, or random coefficients,
                  multinomial logit (MMNL) models for discrete
                  response, and establishes the following
                  results. Under mild regularity conditions, any
                  discrete choice model derived from random utility
                  maximization has choice probabilities that can be
                  approximated as closely as one pleases by a MMNL
                  model. Practical estimation of a parametric mixing
                  family can be carried out by Maximum Simulated
                  Likelihood Estimation or Method of Simulated
                  Moments, and easily computed instruments are
                  provided that make the latter procedure fairly
                  efficient. The adequacy of a mixing specification
                  can be tested simply as an omitted variable test
                  with appropriately defined artificial variables. An
                  application to a problem of demand for alternative
                  vehicles shows that MMNL provides a flexible and
                  computationally practical approach to discrete
                  response analysis.},
 author = {Daniel McFadden and Kenneth Train},
 journal = {Journal of Applied Econometrics},
 number = {5},
 pages = {447--470},
 publisher = {Wiley},
 title = {Mixed MNL Models for Discrete Response},
 volume = {15},
 year = {2000}
}

@Article{FORI:KOPP:93,
  author = 	 {Forinash, C. V. and Koppleman, F. S.},
  title = 	 {Application and interpretation of nested logit
                  models and intercity mode choice},
  journal = 	 {Transportation Record},
  year = 	 {1993},
  OPTkey = 	 {},
  volume = 	 {1413},
  OPTnumber = 	 {},
  pages = 	 {98-106},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Book{FOX:WEIS:19,
    title = {An {R} Companion to Applied Regression},
    edition = {third},
    author = {John Fox and Sandford Weisberg},
    year = {2019},
    publisher = {Sage},
    address = {Thousand Oaks {CA}}
}

@Article{KOPP:WEN:98,
  author = 	 {Koppelman, Franck S. and Wen, Chieh-Hua},
  title = 	 {Alternative nested logit models: structure,
                  properties and estimation},
  journal = 	 {Transportation Research B},
  year = 	 {1998},
  OPTkey = 	 {},
  volume = 	 {32},
  number = 	 {5},
  pages = 	 {289-298},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{ZEIL:CROIS:10,
    title = {Extended Model Formulas in {R}: Multiple Parts and Multiple Responses},
    author = {Achim Zeileis and Yves Croissant},
    journal = {Journal of Statistical Software},
    year = {2010},
    volume = {34},
    number = {1},
    pages = {1--13},
    doi = {10.18637/jss.v034.i01}
}

  @Article{ZEIL:HOTH:02,
    title = {Diagnostic Checking in Regression Relationships},
    author = {Achim Zeileis and Torsten Hothorn},
    journal = {R News},
    year = {2002},
    volume = {2},
    number = {3},
    pages = {7--10}
  }

  @Article{LEIF:13,
    title = {{texreg}: Conversion of Statistical Model Output in {R} to {\LaTeX} and {HTML} Tables},
    author = {Philip Leifeld},
    journal = {Journal of Statistical Software},
    year = {2013},
    volume = {55},
    number = {8},
    pages = {1--24},
    doi = {10.18637/jss.v055.i08}
  }

@Article{SMAL:ROSE:81,
  author = 	 {Small, K. A. and Rosen, H. S.},
  title = 	 {Applied welfare economics with discrete choice
                  models},
  journal = 	 {Econometrica},
  year = 	 {1981},
  OPTkey = 	 {},
  volume = 	 {49},
  OPTnumber = 	 {},
  pages = 	 {105--130},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

  @Manual{MAXLIK:10,
    title = {maxLik: Maximum Likelihood Estimation},
    author = {Toomet, Ott and Henningsen, Arne},
    year = {2010},
    note = {R package version 0.8-0},
    url = {https://CRAN.R-project.org/package=maxLik},
  }

@Article{DALY:87,
  author = 	 {Daly, A.},
  title = 	 {Estimating `tree' logit models},
  journal = 	 {Transportation Research B},
  year = 	 {1987},
  OPTkey = 	 {},
  OPTvolume = 	 {21},
  OPTnumber = 	 {},
  pages = 	 {251-267},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}


@Article{HEIS:02,
  author = 	 {Heiss, Florian},
  title = 	 {Structural choice analysis with nested logit models},
  journal = 	 {The Stata Journal},
  year = 	 {2002},
  OPTkey = 	 {},
  volume = 	 {2},
  number = 	 {3},
  pages = 	 {227-252},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{HENS:GREEN:02,
  author = 	 {Hensher, David A. and Greene, William H.},
  title = 	 {Specification and estimation of the nested logit
                  model: alternative normalisations},
  journal = 	 {Transportation Research Part B},
  year = 	 {2002},
  OPTkey = 	 {},
  volume = 	 {36},
  OPTnumber = 	 {},
  pages = 	 {1-17},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@InProceedings{MCFAD:78,
  author = 	 {McFadden, D.},
  title = 	 {Spatial interaction theory and planning models},
  OPTcrossref =  {},
  OPTkey = 	 {},
  booktitle = {Modeling the choice of residential location},
  pages = 	 {75-96},
  year = 	 {1978},
  editor = 	 {Karlqvist, A.},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTmonth = 	 {},
  OPTorganization = {},
  publisher = {North-Holland, Amsterdam},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Book{TRAI:03,
  author = 	 {Train, Kenneth E.},
  ALTeditor = 	 {},
  title = 	 {Discrete choice methods with simulation},
  publisher = 	 {Cambridge University Press},
  year = 	 {2003},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  address = 	 {Cambridge, UK},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Book{DAGA:79,
  author = 	 {Daganzo, C.},
  ALTeditor = 	 {},
  title = 	 {Multinomial probit: the theory and its application to demand forecasting},
  publisher = 	 {Academic Press, New York},
  year = 	 {1979},
  OPTkey = 	 {},
  OPTvolume = 	 {},
  OPTnumber = 	 {},
  OPTseries = 	 {},
  OPTaddress = 	 {},
  OPTedition = 	 {},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{GEWE:KEAN:RUNK:94,
  author = 	 {Geweke, J. and Keane, M. and Runkle, D.},
  title = 	 {Alternative computational approaches to inference in the multinomial probit model},
  journal = 	 {Review of economics and statistics},
  year = 	 {1994},
  OPTkey = 	 {},
  volume = 	 {76},
  OPTnumber = 	 {},
  pages = 	 {609-632},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{HAUS:WISE:78,
  author = 	 {Hausman, J. and Wise, D.},
  title = 	 {A conditional probit model for qualitative choice: discrete decisions recognizing interdemendence and heterogeneous preferences},
  journal = 	 {Econometrica},
  year = 	 {1978},
  OPTkey = 	 {},
  volume = 	 {48},
  OPTnumber = 	 {},
  pages = 	 {403-429},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@article{KLIN:THOM:96,
 ISSN = {00029092, 14678276},
 abstract = {Nested logit models are increasingly being used to value
                  environmental amenities. The implications of nesting
                  structure, choice of estimator (FIML or sequential),
                  and restrictions on parameters across nested groups
                  on welfare estimates are examined. These issues have
                  received little previous attention. Model selection
                  criteria that can be used to distinguish among the
                  models are extended and applied. Additionally, a
                  simulation procedure is implemented to construct
                  standard errors for the welfare measures.},
 author = {Catherine L. Kling and Cynthia J. Thomson},
 journal = {American Journal of Agricultural Economics},
 number = {1},
 pages = {103-114},
 publisher = {[Agricultural \& Applied Economics Association, Oxford University Press]},
 title = {The Implications of Model Specification for Welfare Estimation in Nested Logit Models},
 volume = {78},
 year = {1996}
}

@article{SARR:DAZI:17,
   author = {Mauricio Sarrias and Ricardo Daziano},
   title = {Multinomial Logit Models with Continuous and Discrete Individual Heterogeneity in R: The gmnl Package},
   journal = {Journal of Statistical Software, Articles},
   volume = {79},
   number = {2},
   year = {2017},
   keywords = {latent class; mixed multinomial logit; random parameters; preference heterogeneity; R},
   abstract = {This paper introduces the package gmnl in R for
                  estimation of multinomial logit models with
                  unobserved heterogeneity across individuals for
                  cross-sectional and panel (longitudinal)
                  data. Unobserved heterogeneity is modeled by
                  allowing the parameters to vary randomly over
                  individuals according to a continuous, discrete, or
                  discrete-continuous mixture distribution, which must
                  be chosen a priori by the researcher. In particular,
                  the models supported by gmnl are the multinomial or
                  conditional logit, the mixed multinomial logit, the
                  scale heterogeneity multinomial logit, the
                  generalized multinomial logit, the latent class
                  logit, and the mixed-mixed multinomial logit. These
                  models are estimated using either the maximum
                  likelihood estimator or the maximum simulated
                  likelihood estimator. This article describes and
                  illustrates with real databases all functionalities
                  of gmnl, including the derivation of individual
                  conditional estimates of both the random parameters
                  and willingness-to-pay measures.},
   issn = {1548-7660},
   pages = {1--46},
   doi = {10.18637/jss.v079.i02}
}

@Article{DALY:HESS:TRAI:12,
author="Daly, Andrew
and Hess, Stephane
and Train, Kenneth",
title="Assuring finite moments for willingness to pay in random
                  coefficient models",
journal="Transportation",
year="2012",
month="Jan",
day="01",
volume="39",
number="1",
pages="19--31",
abstract="Random coefficient models such as mixed logit are
                  increasingly being used to allow for random
                  heterogeneity in willingness to pay (WTP)
                  measures. In the most commonly used specifications,
                  the distribution of WTP for an attribute is derived
                  from the distribution of the ratio of individual
                  coefficients. Since the cost coefficient enters the
                  denominator, its distribution plays a major role in
                  the distribution of WTP. Depending on the choice of
                  distribution for the cost coefficient, and its
                  implied range, the distribution of WTP may or may
                  not have finite moments. In this paper, we identify
                  a criterion to determine whether, with a given
                  distribution for the cost coefficient, the
                  distribution of WTP has finite moments. Using this
                  criterion, we show that some popular distributions
                  used for the cost coefficient in random coefficient
                  models, including normal, truncated normal, uniform
                  and triangular, imply infinite moments for the
                  distribution of WTP, even if truncated or bounded at
                  zero. We also point out that relying on simulation
                  approaches to obtain moments of WTP from the
                  estimated distribution of the cost and attribute
                  coefficients can mask the issue by giving finite
                  moments when the true ones are infinite.",
issn="1572-9435",
doi="10.1007/s11116-011-9331-3"
}

@Article{SWAI:LOUV:93,
  author = 	 {Swait, J. and Louviere, J.},
  title = 	 {The Role of the Scale Parameter in the Estimation
                  and Use of Multinomial Logit Models},
  journal = 	 {Journal of Marketing Research},
  year = 	 {1993},
  OPTkey = 	 {},
  volume = 	 {30},
  OPTnumber = 	 {},
  OPTpages = 	 {305-314},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{MCFAD:74,
  author = 	 {McFadden, D.},
  title = 	 {The Measurement of Urban Travel Demand},
  journal = 	 {Journal of Public Economics},
  year = 	 {1974},
  OPTkey = 	 {},
  volume = 	 {3},
  OPTnumber = 	 {},
  pages = 	 {303-328},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@article{DIPA:JAIN:94,
 ISSN = 07350015,
 abstract = {A random-coefficients logit model that allows for
                  unobserved heterogeneity in brand preferences and in
                  the responses to marketing variables is empirically
                  investigated using household-level panel data. The
                  unknown underlying distribution of unobserved
                  heterogeneity is approximated by a discrete
                  distribution. The results reveal that there is
                  significant unobserved heterogeneity across
                  households and that ignoring its effects results in
                  a downward bias in the parameter estimates of the
                  marketing variables. It is therefore important to
                  account for heterogeneity in both preferences and
                  responses in the absence of any a priori knowledge
                  about the nature of heterogeneity across
                  households.},
 author = {Dipak C. Jain and Naufel J. Vilcassim and Pradeep K. Chintagunta},
 journal = {Journal of Business \& Economic Statistics},
 number = 3,
 pages = {317-328},
 publisher = {[American Statistical Association, Taylor \& Francis, Ltd.]},
 title = {A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data},
 volume = 12,
 year = 1994
}

@Article{CROI:20,
  author = 	 {Croissant, Yves},
  title = 	 {Estimation of Random Utility  Models in R: The mlogit Package"},
  journal = 	 {Journal of Statistical Software},
  year = 	 {2020},
  OPTkey = 	 {},
  volume = 	 {95},
  number = 	 {11},
  pages = 	 {1--41},
  doi =          {10.18637/jss.v095.i11},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{BEGG:CARD:HAUS:81,
  author = 	 {Beggs, S. and Cardell, S. and Hausman, J.},
  title = 	 {Assessing the potential demand for electric cars},
  journal = 	 {Journal of econometrics},
  year = 	 {1981},
  OPTkey = 	 {},
  volume = 	 {16},
  OPTnumber = 	 {},
  pages = 	 {1-19},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{CHAP:STAE:82,
  author = 	 {Chapman, R. and Staelin, R.},
  title = 	 {Exploiting rank ordered choice set data within the stochastic utility model},
  journal = 	 {Journal of marketing research},
  year = 	 {1982},
  OPTkey = 	 {},
  volume = 	 {14},
  OPTnumber = 	 {},
  pages = 	 {288-301},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}

@Article{HAUS:RUUD:87,
  author = 	 {Hausman, J. and Ruud, P.},
  title = 	 {Specifying and testing econometric models for rank-ordered data},
  journal = 	 {Journal of econometrics},
  year = 	 {1987},
  OPTkey = 	 {},
  volume = 	 {34},
  OPTnumber = 	 {},
  pages = 	 {83-103},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}



@Unpublished{REVE:TRAI:00,
  author = 	 {Revelt, D. and Train, K.},
  title = 	 {Customer-specific taste parameters and mixed logit'},
  note = 	 {Working Paper no. E00-274, Department of Economics,
                  University of California, Berkeley},
  OPTkey = 	 {},
  OPTmonth = 	 {},
  year = 	 {2000},
  OPTannote = 	 {}
}

@Article{HUBE:TRAI:01,
  author = 	 {Hubert, J. and Train, K.},
  title = 	 {On the similarity of classical and Bayesian estimates of individual mean pathworths},
  journal = 	 {Marketing Letters},
  year = 	 {2001},
  OPTkey = 	 {},
  volume = 	 {12},
  OPTnumber = 	 {},
  pages = 	 {259-269},
  OPTmonth = 	 {},
  OPTnote = 	 {},
  OPTannote = 	 {}
}
