Project Q1 Announcement

Class,

Please find here a google spreadsheet with data for Q1 - a common dataset that can then be used by the class.

If you already have collected data and estimated a model for Q1, then ignore this post and continue as you had planned.

The issue with secondary data for Q1 as I see it, is that there was little, if any, variation in the monthly price of cars. In fact, nothing on the right hand side of the equation (i.e., in the Xs) varies by month at all. That puts estimation in jeopardy.

So I next ventured to find where I could dig up price data from.

If such data were not available, the logical next step would be to try to find proxies for car price (for example, if I have monthly revenue data, then coupled with units data I can impute the average model price per month).

That is similar to what I ended up doing, but for annual sales data and not the monthly one originally sent.

I found annual unit sales data for cars by brand-model for the last 5 years, found annual revenue increase for the industry from LRC databases and open source data (SIAM website)and imputed the average prices by model in the dataset.

Now you have both units and prices as well as brand information and can easily get basic attribute information by model. IOW, you have all the ingredients for a simple demand estimation exercise. Q1 is now quite straightforward, IMHO. You got the dataset, you next design a simple demand model, and then all you have to do is run regression in SPSS for Q1. Take your call on which observations to keep and which to discard.

Am sure you are busy with exams and stuff. After the exams are done, kindly go through these communications again before doing anything with Q1 in the project.

Shall send another communication after the exam is done. Keep an eye on the blog now and then for more info on the project and the exam.

Sorry that the whole project thing is not as clear-cut and straightforward as it should have been. Now that both the datasets are in place - primary and secondary - data uncertainty is gone. Also, now that you have explicit guidance on what to do with each dataset, the methodological uncertainty is gone too. In case you are not clear about it, read up the project related guidance for the past few days on the blog. I expect things will settle down much more nicely now, IMHO.

Tks.

Sudhir