Month: June 2015

This post is part of a continuing series on The Books that Have Shaped My Thinking.

It’s the summer, so I have time to read, both for work and for pleasure, and I have time to read books instead of just journal articles and blog posts. This made me realize that while a lot of my thinking has been shaped by things that I have read in journal articles (economics is an article-based field) and in blog posts (there is no better means of spreading important ideas quickly), a large part of my thinking has been shaped by books, which often contain more exciting ideas than journal articles–because they face less strict of a review process, books can be more daring in their claims, and thus have more chances of causing you to change how you view the world.

So I decided to write this series of posts on books that shaped my thinking. I talked about development books two weeks ago; I talked about food and agriculture books last week; this week I will talk about food and agriculture. Some recommendations are very general; others are eminently personal. I just hope you can find one or two that will also shape your own thinking. I’m sure I am forgetting a lot of important books I have read and which have also shaped my thinking, but I made this list by taking quick look at the bookshelves in my office. Conversely, some of the books in this list also appeared in my previous post on The Books that Have Shaped My Thinking.

Via Matt Bogard, who has a really good post up titled “Linear Literalism and Fundamentalist Economctrics,” the World Economic Forum website has an interesting piece of popular-press econometrics (!) by Angrist and Pischke titled “Why Econometrics Teaching Needs an Overhaul.” Some choice excerpts:

Hewing to the table of contents in legacy texts, today’s market leaders continue to feature models and assumptions at the expense of empirical applications. Core economic questions are mentioned in passing if at all, and empirical examples are still mostly contrived, as in Studenmund (2011), who introduces empirical regression with a fanciful analysis of the relationship between height and weight. The first empirical application in Hill, Griffiths, and Lim (2011: 49) explores the correlation between food expenditure and income. This potentially interesting relationship is presented without a hint of why or what for. Instead, the discussion here emphasises the fact that “we assume the data… satisfy assumptions SR1-SR5.” An isolated bright spot is Stock and Watson (2011), which opens with a chapter on ‘Economic Questions and Data’ and introduces regression with a discussion of the causal effect of class size on student performance. Alas, Stock and Watson also return repeatedly to more traditional model-based abstraction.

The disconnect between econometric teaching and econometric practice goes beyond questions of tone and illustration. The most disturbing gap here is conceptual. The ascendance of the five core econometric tools–experiments, matching and regression methods, instrumental variables, differences-in-differences and regression discontinuity designs–marks a paradigm shift in empirical economics. In the past, empirical research focused on the estimation of models, presented as tests of economic theories or simply because modelling is what econometrics was thought to be about. Contemporary applied research asks focused questions about economic forces and economic policy.

Continuing the ‘Metrics Monday series, and continuing on last week’s theme of control variables discussed in the de Luca et al. working paper, I wanted to discuss endogenous control variables. Note that a lot of what follows is me thinking out loud, and I may well be mistaken about all of this. If so, I welcome comments exploring this topic.

As always, suppose you have observational data, and you are interested in estimating the causal effect of your variable interest D on your outcome of interest Y, and you also have access to a vector of control variables X. For the sake of argument, let’s assume there is only one control variable in the equation

(1) Y = a + bX + cD + e.

The parameter of interest is c. If you have observational data, then you know that in most cases E(D’e) is different from zero–that is, D is endogenous to Y in equation 1, and c does not capture the causal effect of D on Y.