Multinomial Choice Models Flashcards
What is the estimation of discrete choice models called?
Maximum Likelihood estimation
This looks for the value of p that maximises LL
What is the formula for the likelihood function of the binomial distribution?
What is the formula for the maximum likelihood function?
What is the formula for the log likelihood function?
Why do we calculate a likelihood function in terms of logs?
Because a computer may not be able to detect a very small number from the original likelihood function, so logs replace multiplication by summation to aid the software in finding the maximum value
What are the limitations of the logit model?
What are the properties of the Nested Logit model?
What is the cumulative distribution function for εn = (εn1, . . . , εnJ ) in the nested logit model?
What is the variance for εn = (εn1, . . . , εnJ ) in the nested logit model?
Which models of λk are consistent with utility maximisation?
What is the utility function for n facing j alternatives in the multinomial logit model?
What is the variance of the error term in the multinomial logit model?
π2/6
How do we find the probability of i in the multinomial logit model?
How do we find the probability of i given εni in the multinomial logit model?
How do we calculate εni in the multinomial logit model?