12 Questions
What type of data format can be used for multinomial outcomes?
Both long and wide formats
Which of the following is an example of a multinomial outcome?
Product selection
What are the two types of independent variables in multinomial models?
Alternative-invariant and alternative-variant
What type of model is used with alternative-invariant regressors in multinomial modeling?
Multinomial logit model
Do marginal effects necessarily correspond in sign to coefficients in multinomial modeling?
No
What type of model is used with both alternative-invariant and alternative-variant regressors in multinomial modeling?
Conditional logit model
What does the mixed logit model allow for in multinomial modeling?
Random parameters and correlation across alternatives
How are coefficients interpreted for regressors in conditional and mixed logit models?
As odds ratios
What is the multinomial probit model similar to in multinomial modeling?
Multinomial logit model
How is the marginal effects interpretation expressed in multinomial modeling?
As a percentage increase in probability
What does the mixed logit model produce in multinomial modeling?
Both random parameters coefficients and standard deviation of regressor
What is the dependent variable in multinomial modeling?
Categorical, unordered alternatives
Study Notes
Multinomial, Conditional, and Mixed Models
- Multinomial outcome dependent variable with categorical, unordered alternatives.
- Data recorded in wide or long format with codes for alternatives.
- Examples of multinomial outcomes include insurance contracts, product selection, occupational choice, and fishing mode.
- Two types of independent variables: alternative-invariant and alternative-variant.
- Multinomial logit model used with alternative-invariant regressors, with coefficients and marginal effects.
- Marginal effects do not necessarily correspond in sign to coefficients.
- Conditional logit model used with both alternative-invariant and alternative-variant regressors, with coefficients and marginal effects.
- Mixed logit model allows for random parameters and correlation across alternatives.
- Coefficient interpretation for regressors in both conditional and mixed logit models.
- Multinomial probit model similar to multinomial logit model, but uses standard normal cdf.
- Marginal effects interpretation: each unit increase in independent variable increases/decreases probability of selecting alternative by the marginal effect expressed as a percent.
- Mixed logit model produces random parameters coefficients for both regressor and standard deviation of regressor, indicating heterogeneity in effect of independent variable on alternative chosen.
Multinomial, Conditional, and Mixed Models
- Multinomial outcome dependent variable with categorical, unordered alternatives.
- Data recorded in wide or long format with codes for alternatives.
- Examples of multinomial outcomes include insurance contracts, product selection, occupational choice, and fishing mode.
- Two types of independent variables: alternative-invariant and alternative-variant.
- Multinomial logit model used with alternative-invariant regressors, with coefficients and marginal effects.
- Marginal effects do not necessarily correspond in sign to coefficients.
- Conditional logit model used with both alternative-invariant and alternative-variant regressors, with coefficients and marginal effects.
- Mixed logit model allows for random parameters and correlation across alternatives.
- Coefficient interpretation for regressors in both conditional and mixed logit models.
- Multinomial probit model similar to multinomial logit model, but uses standard normal cdf.
- Marginal effects interpretation: each unit increase in independent variable increases/decreases probability of selecting alternative by the marginal effect expressed as a percent.
- Mixed logit model produces random parameters coefficients for both regressor and standard deviation of regressor, indicating heterogeneity in effect of independent variable on alternative chosen.
Test your understanding of multinomial, conditional, and mixed models with this quiz! Explore the different types of independent variables, including alternative-invariant and alternative-variant, and learn about the multinomial logit, conditional logit, and mixed logit models. Discover how to interpret coefficients and marginal effects, and understand how these models can be applied to a variety of real-world scenarios. Whether you're an aspiring data analyst or a seasoned statistician, this quiz will challenge and expand your knowledge of multin
Make Your Own Quizzes and Flashcards
Convert your notes into interactive study material.
Get started for free
