Upcoming SlideShare
×

# Choice Models

3,403 views

Published on

3 Likes
Statistics
Notes
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Be the first to comment

Views
Total views
3,403
On SlideShare
0
From Embeds
0
Number of Embeds
26
Actions
Shares
0
0
0
Likes
3
Embeds 0
No embeds

No notes for slide

### Choice Models

1. 1. Choice Models det Iria Puyosa
2. 2. Choice Models Family of statistical models that attempt to capture the underlying rational decision process by which individuals choose among different options det
3. 3. Choice Models Multinomial logit model Conditional fixed-effects logit Alternative specific conditional model (McFadden conditional model) Ordit logit model Stereotype logistic model Nested logit model det
4. 4. Choice Models Multinomial logit model The multinomial logit is a choice model for categorical outcomes MLM is intended for use when the dependent variable takes on more than two outcomes and the outcomes have no natural ordering (e. g. university majors, soap brands, political parties) All predictors in the model should measure individual characteristics that are hypothesized to affect the outcome choice det
5. 5. Choice Models Multinomial logit model MLM is a non-linear regression model using maximum likelihood estimation MLM allows the effects of the independent variables to differ for each distinct outcome category By default, the base outcome category is the one most commonly selected but the model may set to use as base category any other that is meaningful for the researcher MLM estimates simultaneously binary logits for all possible comparisons among the outcome categories e x p ( X iβ j) P r(y i = j) = det 1 + ∑ J e x p ( X iβ j)' j
6. 6. Choice Models Conditional fixed-effects logit It is a model for analyzing panel data in which the choice of the outcome of interest changes over time Outcome variable is usually dichotomous It estimates the effects of change on variables measuring individual characteristics It is commonly used when a group characteristic is hypothesized to have effect on the choiche (e.g. health condition, political party affiliation) det
7. 7. Choice Models Conditional fixed-effects logit It incorpores an indicator variable for controling effects from omitted variables that are constant over the data colection period It allows controlling for unobserved heteregeneity when it is constant over time (e.g. gender, ideology) det
8. 8. Choice Models Alternative specific conditional model (McFadden conditional model) In the McFadden conditional logit, outcomes choices may be dichotomous or categorical Outcome choices are expressed as functions of the characteristics of the alternatives themselves as well as functions of characteristics of the choosers (as it occurs in the MLM) det
9. 9. Choice Models Alternative specific conditional model (McFadden conditional model) In the conditional logit, outcomes choices may be dichotomous or categorical Outcome choices are expressed as functions of the characteristics of the alternatives themselves as well as functions of characteristics of the choosers (as it occurs in the MLM) det
10. 10. Choice Models Alternative specific conditional model (McFadden conditional model) Thus, there are two types of independent variables: alternative-specific variables and case-specific variables. Alternative-specific variables vary across both cases and alternatives. They are specified as independent variables. Case-specific variables vary only across cases and are specified in the case option. det
11. 11. Choice Models Ordit logit model The ordit logit works for estimating models in which the outcome is categorical, but it can be naturally ranked from low to high (e. g. high performance, average, and low performance) Conceptually, the dependent variable is hypothesized to be an underlying latent continuous variable that can be observed as ordered groups. Ordit logit is estimated using maximum likelihood det
12. 12. Choice Models Stereotype logistic model Like multinomial logit and ordered logistic models, stereotype logistic models are for use with categorical dependent variables. Stereotype logistic models can be used when the researcher is unsure of the relevance of the ordering, as is often the case when subjects are asked to assess or judge something (e.g. Likert scales for customer satisfaction) Stereotype logistic models can also be used when the researcher suspect that some of the alternatives are similar (e.g. job positions) Stereotype logistic models are estimated using det maximum likelihood
13. 13. Choice Models Nested logit model The nested logit is a choice model for categorical outcomes Nested logit should be used for analyzing models in which the choice has a two-level or three level structure (e. g. deciding first whether or not to buy a car, second why type of car to buy, and third, specific car model) The nested logit model can be explained as the product of a series of MNL choice models det defining each level in a tree structure
14. 14. Choice Models Nested logit model The outcome variable is a tree that specifies all the possible alternatives within the two-level or three-level structure The model is specified in series of equations for each choice level Dependent variables should include case- specific variables (individual characteristics) and alternative-specific variables (choice characteristics) det
15. 15. Choice Models For all choice models The estimation results are reported as beta coefficients or odds ratios Post-estimation techniques generate predicted probabilities for specific individuals profiles, discrete changes in probabilities and factors changes in the odds depending on the change of the value of any specific variable det
16. 16. Choice Models Multinomial logit model Conditional model fixed-effects (Luce conditional model) Alternative specific conditional model (McFadden conditional model) Ordit logit model Stereotype logistic model Nested logit model det
17. 17. Minimal bibliography Cameron, A. C. and P. Trivedi (2009). Microeconometrics Using Stata. College Station, Texas, Stata Press. Koppelman F & Sethi V (2000) Closed-form discrete-choice models. In: Hensher DA & Button KJ (eds) Handbook of Transport Modelling, Volume 1, of Handbooks in Transport (pp 211–222). Oxford: Pergamon Press. Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Thousands Oaks, CA: Sage Publications. Long, J.S., and Freese, J. (2001). Regression Models for Categorical Dependent Variables Using Stata. College Station, TX: A Stata Press Publication. McFadden, D. (1978) Modeling the Choice of Residential Location. Transportation Research Record 672, TRB, National Research Council, Washington, D.C., pp.72-77.
18. 18. Choice Models Iria Puyosa info@formacomuna.org