• The outlook for the US stock market looks unusually poor.

• I don’t have an answer to this question, but I’ve gathered a few related stats that helped to get my own thinking into order.

• The most fundamental technique in statistical learning is ordinary least squares (OLS) regression. If we have a vector of observations $$y$$ and a matrix of features associated with each observation $$X$$, then we assume the observations are a linear function of the features plus some (iid) random noise, $$\epsilon$$:

• Countries around the world are going into more-or-less complete states of lockdown in an effort to stop the spread of novel coronavirus. The question I find myself asking is whether the obvious economic cost of this can possibly justify the benefits. I built a model to answer this question and I find that, contrary to my priors, lockdown is actually justifiable under reasonable assumptions.

• What factors influence house prices? This is a perennial topic of dinner party discussion, but the standard of the debate rarely rises above offering anecdotal evidence. Frustrated with the status quo, I decided to tackle the question with statistics. In this post I look at which macroeconomic factors are associated with future house price rises or falls.