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To do well in an econometrics or statistics course at any level, you need to have a large number of simple properties of random variables at your fingertips. Some years back I made a handout containing the most important properties for my undergraduate students at the University of Pennsylvania.

In econometrics it’s absolutely crucial to keep track of which things are dependent and which are independent. To make this as confusing as possible for students, a typical introductory econometrics course moves back and forth between different notions of dependence, stopping occasionally to mention that they’re not equivalent but never fully explaining why, on the premise that “you’ve certainly already learned this in your introductory probability and statistics course.

The Poisson distribution is the most famous probability model for counts, non-negative integer values. Many real-world phenomena are well approximated by this distribution, including the number of German bombs that landed in 1/4km grid squares in south London during WWII.

If you study enough econometrics, you will eventually come across an asymptotic argument in which some parameter is assumed to change with sample size. This peculiar notion goes by a variety of names including “Pitman drift,” a “sequence of local alternatives,” and “local mis-specification,” and crops up in a wide range of problems from weak instruments, to model selection, to power analysis.

In this post we’ll examine a very simple instrumental variables model from three different perspectives: two familiar and one a bit more exotic. While all three yield the same solution in this particular model, they lead in different directions in more complicated examples.