An Introduction to Mixed Logit Model
1
Citation
0
Reference
10
Related Paper
Citation Trend
Abstract:
사회과학에서 널리 쓰이는 범주형 데이터, 특히 범주형 종속변수의 분석에 있어 로짓모델은 지난 수 십년간 매우 널리 사용되어왔다. 특히, 다항로짓(multinomial logit)과 조건부로짓(conditional logit)은 종속변수가 3항이상의 명목형 범주로 이루어져 있는 경우 매우 유용한 것이 사실이다. 하지만 이 모델들은 널리 알려진 한계가 있다. 첫째, 개인들 간의 선호도의 동질성의 가정, 둘째, 무관한 대안으로 부터의 독립성(independence of irrelevant alternatives)의 가정, 셋째, 시간별, 개인별 무상관성(uncorrelatedness across time and individual)의 원칙의 가정이 그것이다. 시뮬레이션에 기반한 기법이 확산되기 시작한 이래 범주형 데이터 연구에서 새롭게 활발히 개발, 적용중인 방법중 하나인 혼합로짓(mixed logit)은 개인간 불균질한 선호도(heterogeneous preferences), 교체패턴(substitution patterns)의 자유로운 설정, 시간 및 개인간 상관성 등을 범주형 모델링 과정에 포함시킬 수 있는 가능성을 제시함으로써 전통적으로 사용되던 범주형 데이터 분석의 한계의 극복 뿐만아니라 보다 심화된 데이터 분석을 제시 할 수 있는 방법을 제시한다. 본 논문의 목적은 혼합 로짓을 소개하는 것이다.Keywords:
Mixed logit
Independence
Multinomial probit
Substitution (logic)
Multinomial probit
Probit
Ordered probit
Mixed logit
Multivariate probit model
Multinomial distribution
Cite
Citations (64)
Multinomial probit
Mixed logit
Probit
Ordered probit
Discrete choice
Cite
Citations (10)
Mixed logit
Multinomial probit
Discrete choice
Cite
Citations (28)
Series Editor's Introduction Preface 1. Introduction 2. Ordered Models/ Introduction Introduction Methodology Application to Deprivation Status Estimation Over Subsamples: Characteristics versus coefficients 3. Multinomial Logit / Introduction A Random Utility Model The Class of Logit Models: Multinomial and conditional Multinomial Logit Application to Occupational Outcomes Conditional Logit and the Independence of Irrelevant Alternatives 4. Program Listings References
Multinomial probit
Ordered probit
Mixed logit
Probit
Multinomial distribution
Independence
Cite
Citations (369)
Empirical studies on household car ownership have used two types of discrete choice modeling structures: ordered and unordered. In ordered response structures, such as the ordered logit and ordered probit models, the choice of the number of household vehicles arises from a unidimensional latent variable that reflects the propensity of a household to own vehicles. Unordered response structures are based on the random utility maximization principle, which assumes a household associates a utility value across different car ownership levels and chooses the one with the maximum utility. The most common unordered response models are the multinomial logit and probit models, but only the multinomial logit has been used in practical applications because of its simple structure and low computational requirements. Consensus among researchers on unordered or ordered structures is still lacking. Empirical studies have reported various models, including the multinomial logit, ordered logit, and ordered probit. An open question remains: Which model would better reflect households’ car ownership choices? This paper compares multinomial logit, ordered logit, and ordered probit car ownership models through a number of formal evaluation measures and empirical analysis of three data sets: the 2001 National Household Travel Survey for the Baltimore [Maryland] Metropolitan Area, the 2005 Dutch National Travel Survey, and the 2000 Osaka [Japan] Metropolitan Person Trip Data. Results show the multinomial logit model should be selected for modeling the level of household car ownership.
Multinomial probit
Ordered probit
Probit
Discrete choice
Mixed logit
Multivariate probit model
Car ownership
Ordered logit
Cite
Citations (77)
Mixed logit
Cite
Citations (1)
Multinomial probit
Probit
Ordered probit
Mixed logit
Identification
Specification
Multinomial distribution
Cite
Citations (71)
Multinomial probit
Pooling
Mixed logit
Multinomial distribution
Cite
Citations (7)
Multinomial probit
Mixed logit
Probit
Ordered probit
Discrete choice
Binary logit model
Econometric model
Multivariate probit model
Cite
Citations (0)
We focus on the determinants of labor choices in the presence of partially microsimulated data and discrete choice sets not identical for all agents under examination. The independence of irrelevant alternative assumption is thus discussed and the variability of the available choice set is taken into account. By comparing a Bayesian mixed multinomial logit model to a model without random effects, we show how the above described scenario affects labor choices made by single females and females within couples when the same discrete choice set is assigned to both individuals in each couple and the partner’s choice is known.
Mixed logit
Discrete choice
Multinomial probit
Multinomial distribution
Choice set
Independence
Cite
Citations (0)