Here is a post by a student in EEP100 at Berkeley on the aguanomics blog. This is the first of a series of posts by students.

I’m considering implementing this for my PSTAT 262MC class on Applied Bayesian Time Series. My thought is to require students to post twice during the quarter. The first post will try to forecast a time series using just intuition, i.e. no math/stats. The second post will try to forecast a time series (possibly the same one) using a statistical model based approach.

Any thoughts?

I was perusing Bobby Gramacy’s introductory slides for his Bayesian Inference course here at UCSB. On slide 11, he mentions the saying “All models are wrong, but some are useful.” Googling for this phrase, I found a post by Andrew Gelman about this phrase where he reiterates the point that all models are wrong and the goal of posterior model checking is “to understand what aspects of the data are captured by the model and what aspects are not.”

My addition to this conversation focuses on the second half of the saying. The fact that all models are wrong should not discourage anybody from trying to model in the first place. Instead, modelers need to understand what scientific question is being asked and build a model to answer that question. A model will be useful if it can answer the question of interest.