View in EDS

Estimation and welfare analysis from mixed logit models with large choice sets.

Saved in:
Bibliographic Details
Title: Estimation and welfare analysis from mixed logit models with large choice sets.
Authors: von Haefen, Roger H.1 roger_von_haefen@ncsu.edu, Domanski, Adam2 domanski@econw.com
Source: Journal of Environmental Economics & Management. Jul2018, Vol. 90, p101-118. 18p.
Subjects: Welfare economics, Logits, Statistical sampling, Expectation-maximization algorithms, Parameter estimation, Discrete choice models
Abstract: We show how McFadden's sampling of alternatives approach and the expectation-maximization (EM) algorithm can be used to consistently estimate latent-class, mixed logit models in applications with large choice sets. We present Monte Carlo evidence confirming our approach works well in small samples, apply the method to a dataset of Wisconsin angler site destination choices, and report welfare estimates for several policy scenarios. Of interest to applied researchers, our results quantify the tradeoffs between model run-time, accuracy, and precision of welfare estimates associated with samples of different sizes. Moreover, although our results confirm that larger efficiency losses arise with smaller samples as theory would predict, they also suggest that depending on researcher needs, random samples as small as 28 alternatives (5% of the full set of alternatives in our application) can produce relatively accurate welfare estimates that are useful for exploratory modeling, sensitivity analysis, and policy purposes. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Environmental Economics & Management is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 130951108
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Estimation and welfare analysis from mixed logit models with large choice sets.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22von+Haefen%2C+Roger+H%2E%22">von Haefen, Roger H.</searchLink><relatesTo>1</relatesTo><i> roger_von_haefen@ncsu.edu</i><br /><searchLink fieldCode="AR" term="%22Domanski%2C+Adam%22">Domanski, Adam</searchLink><relatesTo>2</relatesTo><i> domanski@econw.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Journal+of+Environmental+Economics+%26+Management%22">Journal of Environmental Economics & Management</searchLink>. Jul2018, Vol. 90, p101-118. 18p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Welfare+economics%22">Welfare economics</searchLink><br /><searchLink fieldCode="DE" term="%22Logits%22">Logits</searchLink><br /><searchLink fieldCode="DE" term="%22Statistical+sampling%22">Statistical sampling</searchLink><br /><searchLink fieldCode="DE" term="%22Expectation-maximization+algorithms%22">Expectation-maximization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+choice+models%22">Discrete choice models</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: We show how McFadden's sampling of alternatives approach and the expectation-maximization (EM) algorithm can be used to consistently estimate latent-class, mixed logit models in applications with large choice sets. We present Monte Carlo evidence confirming our approach works well in small samples, apply the method to a dataset of Wisconsin angler site destination choices, and report welfare estimates for several policy scenarios. Of interest to applied researchers, our results quantify the tradeoffs between model run-time, accuracy, and precision of welfare estimates associated with samples of different sizes. Moreover, although our results confirm that larger efficiency losses arise with smaller samples as theory would predict, they also suggest that depending on researcher needs, random samples as small as 28 alternatives (5% of the full set of alternatives in our application) can produce relatively accurate welfare estimates that are useful for exploratory modeling, sensitivity analysis, and policy purposes. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Journal of Environmental Economics & Management is the property of Academic Press Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=130951108
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.jeem.2018.05.002
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 101
    Subjects:
      – SubjectFull: Welfare economics
        Type: general
      – SubjectFull: Logits
        Type: general
      – SubjectFull: Statistical sampling
        Type: general
      – SubjectFull: Expectation-maximization algorithms
        Type: general
      – SubjectFull: Parameter estimation
        Type: general
      – SubjectFull: Discrete choice models
        Type: general
    Titles:
      – TitleFull: Estimation and welfare analysis from mixed logit models with large choice sets.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: von Haefen, Roger H.
      – PersonEntity:
          Name:
            NameFull: Domanski, Adam
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 00950696
          Numbering:
            – Type: volume
              Value: 90
          Titles:
            – TitleFull: Journal of Environmental Economics & Management
              Type: main
ResultId 1