Multinomial Logit Model

Multinomial Logit Model Specification

A multinomial logit model for estimating multiple vehicle crash severity outcomes was employed due to the flexibility of the multinomial logit model over the ordered model and its frequent use in similar works previously published. Violations of the IIA property were also tested to ensure the proper functional form was, indeed, utilized. Information on each heavy

truck driver and vehicle involved in a multiple vehicle accident in Iowa from 2007-2012 was input into the model. In addition, a restricted set of information on the other vehicle(s) and driver(s) involved in a crash with a heavy truck was joined to each heavy truck crash observation through a relate in ArcMap 10.1 GIS software. For the multiple vehicle crash model, the three injury categories considered were no injury (property damage only), possible or minor injury, and fatal or major injury. The choice of outcome categories was based on observations made during data description and categories used in past studies (Morgan and Mannering, 2011; Gkritza et al., 2010).

For each crash severity outcome the multinomial logit model function takes the form

(2)

where is the function that determines discrete severity outcome for observation is

a vector of estimable parameters for severity outcome , is a vector of observable characteristics (driver, crash, vehicle, environment, etc.) that determine the severity of crash , and is an error term to compensate for possible omitted variables, improper functional

form specification, use of proxy variables, and variations in from one observation to the next, not accounted for (Washington et al. 2011).

If are assumed to be extreme value type 1 (Gumbel) distributed the multinomial logit model takes the form

( ) [ ]

∑ [ ] (3)

where ( ) is the probability of crash resulting in severity outcome , with representing the set of all possible crash severity outcomes.

Multinomial Logit Elasticity Estimation

To fully assess the vector of estimated coefficients ( ), elasticities were computed. Elasticities are a measure of the magnitude of impact a particular variable has on outcome probabilities and can be calculated from the partial derivative of each observation ( subscripting omitted): ( ) ( ) ( ) (4)

where ( ) is the probability of outcome and is the value of variable for outcome .

Using Eq. (3) and Eq. (4) gives

( ) _{[ ( )]}

where is the estimated coefficient associated with variable . Elasticity values can be interpreted as the percent effect that a 1 percent change in has on the crash severity outcome probability ( ).

Cross-elasticities are a measure of the effect a variable influencing outcome j has on the probability of crash severity outcome i

( ) _{( )}

(6)

where P(j) is the probability of severity outcome j and is the coefficient of variable .

Cross- elasticities can be interpreted at the percent effect that a 1 percent change in has

on the crash severity outcome probability ( ). It is worth noting that Eq. (6) implies that there is one cross-elasticity for all severity outcomes i (i≠j). This property of uniform cross- elasticities is an artifact of the independence of error terms assumed to derive the

multinomial logit model (Washington et al., 2011).

Caution needs to be exercised when computing elasticities. Equations (3) to (5) do not apply to indicator variables (variables that take the value of 0 or 1). To gauge the magnitude of the effect of an indicator variable, pseudo elasticities need to be calculated using

[ ] [ ]

[ ] (7)

where [ ] is the probability of outcome i (direct elasticity) or j (cross elasticity) given and [ ] is the probability of outcome i (direct elasticity) or j (cross elasticity) given . The pseudo elasticity thus represents the

percentage change in the probability of a severity outcome when an indicator variable is changed from 0 to 1 (Geedipally et al., 2011).

It should be noted that in case of a variable that is included in more than one utility function, the net effect of the variable can be determined by considering both direct and cross elasticities that are estimated for the variable of interest.

Multinomial Logit Goodness of Fit Measures

The goodness of fit of the multinomial logit model can be found by estimating a ρ2

statistic using

where LL(β) is the log-likelihood at convergence and LL(0) is the log-likelihood with all parameters set to zero. The perfect model would have a ρ2

statistic equal to one, so the closer the value of ρ2

is to one, the more variance the model is explaining (Washington et al. 2011). The disadvantage of the ρ2 _{statistic is that the value of ρ}2

will always improve with the

addition of parameters, regardless of parameter significance. To account for this, an adjusted ρ2

value can be computed using

( ) _{( )} (9)

where the log -likelihood values are as discussed before and k is the number of parameters in the model.

Multinomial Logit Tests of Significance

One final note to consider when interpreting the results of a multinomial logit model is the assumed distribution of error terms. For computational convenience, error term distribution is assumed to be extreme value type 1 (Gumbel) distributed, not normally distributed. This assumption complicates the interpretation of the multinomial logit model’s results, but only minimally as is demonstrated in Figure 4-2 by showing the similarity between the assumed logit distribution and the normal distribution.

Figure 4-2: Comparison of Binary Logit and Probit Outcome Probabilities (Washington et al. 2001)

For the multinomial logit model the significance of individual parameters is

approximated using a one-tailed t-test to asses if a parameter is significantly different from zero. The test statistic t*, which is approximately t distributed, is

_{. .( )} (10)

where S.E.(β) is the standard error of the parameter. Note that because the multinomial logit model is derived using an extreme value distribution, as discussed in the previous paragraph, the use of t-statistics is not strictly correct, but a reliable approximation in practice

(Washington et al. 2011).

Another more universal and appropriate test for multinomial logit models is the likelihood ratio test. The likelihood ratio test can be used to assess: the significance of individual parameters, a model’s overall significance, and the use of separate parameters for the same variable in multiple outcomes (Washington et al. 2011). The likelihood ratio test is

[ ( ) ( )] (11)

where LL(βR) is the log-likelihood at convergence of the restricted model and LL(βU) is the

log-likelihood at convergence of the unrestricted model. To test generic attributes, LL(βR) is

replaced with the log-likelihood at convergence of the model with generic attributes and LL(βU) is replaced with the log-likelihood at convergence of the model with alternative

specific attributes. In either case the likelihood ratio statistic is χ2

distributed with degrees of freedom equal to the difference in the number of parameters in the restricted/generic model and the unrestricted/alternative specific model.

As mentioned previously, the IIA assumption was also tested to ensure that the

assumption holds for the entire choice set and that the proper functional form was, in fact, specified. To check the IIA assumption, Small and Hsiao propose the following test

( ) [ ( ) ( )] (12)

Where N is the number of observations in the unrestricted choice set, N1 is the number of

observations in the restricted choice set, and α is a scalar greater than 1 based on the ratio of the covariance matrix of the restricted model and the corresponding elements of the

degrees of freedom equal to the number of parameters in the restricted model. A test value below the critical χ2 value confirms the logit model structure cannot be rejected (Ben-Akiva and Lerman, 1985).