Category: Econometrics

Difference-in-discontinuities method

Posted on by Rhys Williams

This post will review the econometric method of difference-in-discontinuities design, outlining the technical details, the strength and weaknesses, and provide an intuitive explanation for how this method can be used to identify causal effects of a treatment policy. In doing so, we will refer to its application in Ferguson and Kim (2023). Firstly, we outline the empirical question posed by Ferguson and Kim (2023) and discuss some potential ways of answering this question, to then motivate the use of the difference-in-discontinuities design approach.

Ferguson and Kim (2023) apply the difference-in-discontinuities technique to answer the question of whether a policy of decentralised agricultural production (called the Household Responsibility System or HRS) can causally explain the increase in agricultural yields seen in China in the late 1970s and 1980s. [...]

The Difference-in-Difference Revolution

Posted on by Rhys Williams

I don’t often post about econometric topics, as I am not an econometrician, but as an applied microeconomist, I have been reading a lot about the difference-in-difference revolution and the new approaches taken to deal with staggered adoption. As the best way to learn something is to teach it, I thought I’d write a blog post explaining my understanding of the problem and the various solutions available. As I am not an econometrician, I will be mainly explaining this through intuition and will add links to the various sources for those interested in a deeper dive on the technical details.

The Problem

For a long time, empiricists would use the two-way fixed effects estimator to estimate linear regressions of interest on a panel dataset of interest. [...]

Interpreting regression coefficients

Posted on by Rhys Williams

In this article I will explain how to interpret regression coefficients when dealing with variables that have been logged.

Why would we work with logged variables? Firstly, we might take the log of a non-linear model, to make it linear in parameters, to satisfy the Gauss-Markov assumptions (these are required for the OLS method of estimation to be the BLUE – the best linear unbiased estimator). Secondly, as we will see below, it can sometimes be easier to interpret coefficients which have been logged, as we can talk about percentage changes, which might make more sense than talking about unit changes, in some contexts. [...]