Due: Nov 16 by 11:59pm

Weight: This assignment is worth 3% of your final grade.

Purpose: The purpose of this assignment is to introduce the concept of preference heterogeneity - that is, not everyone has the same preferences. We will see two ways to account for this: 1) specify preference parameters as distributions, and 2) estimate fixed parameter models on different sub-groups in your data.

Assessment: This assignment is graded using a check system:

Notice that this is essentially a pass/fail system. I’m not grading your writing ability and I’m not counting the number of words you write - I’m looking for thoughtful engagement.

1. Getting Organized

Download and edit this template when working through this assignment. Notice that this week’s template is a .Rmd file.

2. Readings

Up until this point in the class, we have only estimated relatively simple models that assume everyone in our sample has the same preferences. That is, we only estimate a “fixed parameter” model that reflects the average preferences for the whole sample. In reality, preferences are usually heterogeneous (different people have different preferences).

One way to account for this is to estimated a “mixed” logit model where we assume the preference parameters in our utility models follow some distribution across the sample (usually a normal distribution). Another way is to estimate a fixed parameter model on different sub-groups in our data. Each of these approaches are discussed in this week’s video on heterogeneity. Take notes as you watch the video and answer the practice questions in the video as part of your reflection. You may submit your answers however you wish, e.g. hand-write them on paper and take a picture and / or type answers in your reflection .Rmd file.

Click here to download the slides in the video as a PDF.

3. Reflect, Knit, & Submit

Reflect on what you’ve learned and any questions or points of confusion you have about introducing preference heterogeneity into utility models. Is there anything that jumped out at you? Anything you found particularly interesting or confusing? After reflecting, do the following:

EMSE 6035: Marketing Analytics for Design Decisions (Fall 2021)
Wednesdays | 6:10 - 8:40 PM | SEH 7040 | Dr. John Paul Helveston | jph@gwu.edu