ECOM6004: The X?Matrix Fixed - Unordered Multinomial Method - Economics Assignment Help

Assignemnt Task:

The Stata data file, V i c_t r i p_d at a.d t a contains information on transportation trips made in and around Melbourne. Our aim here is to try and explain the transport mode as a function of both the individual and the trip itself. Firstly, do some data cleaning: 

• remove any observations corresponding to taxi, walking or motorcycle journeys (t axi, w al king or motor c yc le = 1), as these numbers are very small and also somewhat atypical compared to the other journeys. 

• Define a categorical variable defining the nature of the journey, using: bus (publ i cbus; 0); t r am (1); t r ain (2); car (vehi c led r i ver , vehi c lepasseng er ; 3); and push bike bi c yc le (4); 

• Create a dummy variable f emale from the string variable sex which has labels of “Female” and ”Male”. 

• Create a dummy variable f ul l_t ime from the string variable mainac t i v i t y based on the label “Full-time work”. 

• Appropriately scale and rename the ag e variable. 

1. Conduct a small exploratory data analysis (ED A), focussing on the variables noted above, along with: log-of trip distance (lnd i st ance); and peak (a dummy for travel in peak hour). 

2. Using bus as the base-case, estimate a Multinomial Logit model of travel mode, against your age variable, as well as lnd i st ance, peak, f emale and f ul l_t ime. 

3. Estimate a full set of partial effects for this model and carefully: a) explain how you calculated these 

b) pick an interesting selection of these, and interpret them 

4. Report these results as relative risk ratios; and comment on and interpret a cou ple of these (differing from those you discussed as partial effects). 

5. Extend your model to allow for ag e to have a nonlinear effect on the underlying utility equations. 

a) Explain how you did this and justify your approach and report your results [1 mark] 

b) Show how you would construct a single partial effect for this model and calculate, with workings, this for a single partial effect 

c) For an appropriate range of age, plot probability profiles as to how the probabilities of all of the travel modes vary with age. 

6. What are the limitations of the generic modelling approach you’ve adopted here (in Section 2)? Why would/wouldn’t you expect it/them to be of issue in this particular example? And finally, how might you test for it/them?

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