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      Interpreting Discrete Choice Models

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      SKU 9781108819404 Categories ,
      In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. This Element describes techniques for the ...

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      Description

      Product ID:9781108819404
      Product Form:Paperback / softback
      Country of Manufacture:GB
      Series:Elements in Quantitative and Computational Methods for the Social Sciences
      Title:Interpreting Discrete Choice Models
      Authors:Author: Garrett Glasgow
      Page Count:75
      Subjects:Research methods: general, Research methods: general, Social research and statistics, Politics and government, Probability and statistics, Data mining, Social research & statistics, Politics & government, Probability & statistics, Data mining
      Description:In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. This Element describes techniques for the interpretation of models.
      In discrete choice models the relationships between the independent variables and the choice probabilities are nonlinear, depending on both the value of the particular independent variable being interpreted and the values of the other independent variables. Thus, interpreting the magnitude of the effects (the “substantive effects”) of the independent variables on choice behavior requires the use of additional interpretative techniques. Three common techniques for interpretation are described here: first differences, marginal effects and elasticities, and odds ratios. Concepts related to these techniques are also discussed, as well as methods to account for estimation uncertainty. Interpretation of binary logits, ordered logits, multinomial and conditional logits, and mixed discrete choice models such as mixed multinomial logits and random effects logits for panel data are covered in detail. The techniques discussed here are general, and can be applied to other models with discrete dependent variables which are not specifically described here.
      Imprint Name:Cambridge University Press
      Publisher Name:Cambridge University Press
      Country of Publication:GB
      Publishing Date:2022-05-12

      Additional information

      Weight138 g
      Dimensions152 × 227 × 9 mm