Due: November 15 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:

  • ✔+ (110%): Responses shows phenomenal thought and engagement with the course content. I will not assign these often.
  • ✔ (100%): Responses are thoughtful, well-written, and show engagement with the course content. This is the expected level of performance.
  • ✔− (50%): Responses are hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

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. One or two sentences is not enough. Write at least a paragraph and show me that you did the readings assigned.

1. Get Organized

Download and edit this template when working through this assignment.

Then unzip the template folder (make sure you unzip it!), then open the .Rproj file to open RStudio. Open the hw10.Rmd file, take notes, and write some example code as you go through the following.

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

Reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing? Write at least a paragraph in your hw10.Rmd file. Here are some suggestions:

  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
  • Write about the messiest data you’ve seen.
  • Connect the course content to your own work or project you’re working on.

4. Knit

Click the “knit” button to compile your hw10.Rmd file into a html web page. Then open the hw10.html file in a web browser and proofread your report. Does all of the formatting look correct?

5. Submit

To submit this assignment, create a zip file of all the files in your R project folder for this assignment. Name the zip file hw10-netID.zip, replacing netID with your netID (e.g., hw10-jph.zip). Then copy that zip file into the “submissions” folder in your Box folder created for this class.