EVALUATION OF MIXED LOGIT AS A PRACTICAL MODELING ALTERNATIVE

This paper describes how, in the context of choice modeling, the approach mostly used s based on random utility theory. According to this theory, each individual has a utility function associated to each of the alternatives, choosing the one which maximizes his/her utility. This individual function can be divided into a systematic component, which considers the effect of the explanatory variables, and a random component that takes into account all of the effects not included in systematic component of the function. For example, the incapacity of the modeler to observe all the variables that have an influence in the decision, measurement errors, differences between individuals, incorrect perceptions of attributes and the randomness inherent to human nature. Depending on the assumptions made for the distribution of the random error term, different models can be derived. The paper presents the Multinominal Logit Model and the Probit Model and it evaluates the Mixed Logit model as a practical modeling alternative.

[1]  Staffan Algers,et al.  Mixed Logit Estimation of the Value of Travel Time , 1998 .

[2]  Marcela Munizaga,et al.  ON THE ERROR STRUCTURE OF DISCRETE CHOICE MODELS. , 1997 .

[3]  H. Williams.  On the Formation of Travel Demand Models and Economic Evaluation Measures of User Benefit , 1977 .

[4]  H. Williams,et al.  Behavioural theories of dispersion and the mis-specification of travel demand models☆ , 1982 .

[5]  Paul A. Ruud,et al.  Handbook of Econometrics: Classical Estimation Methods for LDV Models Using Simulation , 1993 .

[6]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[7]  V. Hajivassiliou,et al.  Smooth unbiased multivariate probability simulators for maximum likelihood estimation of limited dependent variable models , 1993 .

[8]  N. S. Cardell,et al.  Measuring the societal impacts of automobile downsizing , 1980 .

[9]  Hugh F. Gunn,et al.  Statistical aspects of travel demand modelling , 1982 .

[10]  K. Train,et al.  Forecasting new product penetration with flexible substitution patterns , 1998 .

[11]  Moshe Ben-Akiva,et al.  DRAFT Specification , Identification , & Estimation of the Logit Kernel ( or Continuous Mixed Logit ) Model * , 2001 .

[12]  D. Bolduc.  GENERALIZED AUTOREGRESSIVE ERRORS IN THE MULTINOMIAL PROBIT MODEL , 1992 .

[13]  Marcela Munizaga,et al.  NESTED LOGIT MODELLING: SOME HARD FACTS , 1999 .

[14]  Carlos F. Daganzo,et al.  Multinomial Probit: The Theory and its Application to Demand Forecasting. , 1980 .

[15]  J. Boyd,et al.  THE EFFECT OF FUEL ECONOMY STANDARDS ON THE U.S. AUTOMOTIVE MARKET: AN HEDONIC DEMAND ANALYSIS , 1980 .

[16]  D. McFadden.  Conditional logit analysis of qualitative choice behavior , 1972 .

[17]  Mark D. Uncles,et al.  Discrete Choice Analysis: Theory and Application to Travel Demand , 1987 .

[18]  K. Train,et al.  Mixed Logit with Repeated Choices: Households' Choices of Appliance Efficiency Level , 1998, Review of Economics and Statistics.

[19]  K. Train.  Halton Sequences for Mixed Logit , 2000 .

[20]  Marcela Munizaga,et al.  ON THE APPLICABILITY OF THE MULTINOMIAL PROBIT MODEL , 1997 .