Videos and questions for Chapter 3a of the course "Market Analysis with Econometrics and Machine Learning" at Ulm University (taught by Sebastian Kranz)

Could we in principle estimate the utility function as a linear regression via ordinary least squares with a given discrete choice data set?

What will we assume for the distribution of the random utility shocks \(\varepsilon_{nj}\) in a conditonal logit model?

If we increase the variance what happens for consumer 3's choice probabilities of both products in our example?

Which product will consumer 3 pick given the utility values U from the example in the video?

You may remember from your microeconomic classes different classifications of utility functions, like *ordinal utility functions* or *cardinal utility functions*.

What does a cardinal utility function in general represent?

That's all for the videos and slides.

In the RTutor problem we will apply the conditional logit model for a discrete choice data set about heating systems in Californian houses. Besides just exploring the preferences and trade-offs between investment and operational cost, we will use our estimated model to make a prediction of how an investment subsidy to an environmental friendly heating system will affect resulting market shares.