1. Begin by looking at the contents of each dataset using Proc Contents. Report what you observe. Compute the summary statistics for the characteristics of each product by store. Do you find any patterns? Use the panel dataset to compute the share of each included product. (3/5)
2. Define the total number of product J and stores S in the choice set. Write a data step to create and assign a unique id called JID to each product and a unique SID to each store. Hint: JID=1,2,3...J; SID=1,2,...S. Based on your understanding of the data, define the number of levels and the choices made in each nest. (3/5)
3. Assemble the choice set for each week. Use the product and prodchar dataset to create a choice set for each week. The choice set for a week should contain all stores, products in each store and their attributes in that week. Create alternative specific intercepts for each alternative in the choice set. Write out a matrix where the rows correspond to the alternatives and columns correspond to the intercepts. Write a data step to include the intercepts in the choice set dataset. Merge the weekly choiceset data to the panel dataset and introduce a binary dummy variable bought to indicate the chosen product in the choice set. (3/5)
4. Write out the utility functions for a nested logit model with only the alternative specific intercepts for the lower level nests and only the inclusive values for the upper level nests. Specify the nested logit model in Proc MDC. Estimate the model and interpret the results. What can you conclude from the coefficients for the inclusive value terms? (3/5)
5. Add price as a covariate in the utility functions at the lower level. Write out the utility functions and estimate the model. Comment on your results. (3/5)
6. Add the display variable to the model. Write out the utility functions and estimate the model. Comment on your results. (3/5)
7. Add the feature variable to the model. Write out the utility functions and estimate the model. Comment on your results. (3/5)
8. Estimate a conditional logit model with 12 alternatives, price and display. Comment on the estimated parameters (5)
9. (Optional) Invert the model in Questions 6. Build a nested logit model where consumers choose the product first and then store for each product. Comment on your results. (0)
10. Tabulate the loglikelihood, AIC and BIC values for the estimated models. Which model would you select for subsequent analysis and why? (5)
11. Assume the the product manager of Dannon had given you this data with the hope that you would help him understand the market. Based on your analysis what would you report to the manager?
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