The Books that Have Shaped My Thinking: Econometrics

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 three weeks ago; I talked about food and agriculture books two weeks ago; and I talked about economic theory books last week; this week I will talk about econometrics. 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.

Josh Angrist and Steve Pischke, Mastering ‘Metrics. Perhaps the single, most concise statement of how empirical work is currently being done in applied microeconomics (i.e., labor, development, health, urban, environmental, law economics, etc.) This book is so well written it can be read by economists and noneconomists alike, and any smart undergrad can read it and learn something about how we learn from real-world data in economics.

Josh Angrist and Steve Pischke, Mostly Harmless Econometrics. This is Angrist and Pischke’s earlier, more technical book, which essentially presents the same concepts as their more recent book, but with the necessary technical details. Even then, the book is easy to read (for an econometrics text, that is), and highly informative.

Angust Deaton, The Analysis of Household Surveys. Though this book isn’t strictly about econometrics, Deaton presents a good review of the core concepts in econometrics. Perhaps more importantly, this book is about how to collect data from household surveys and construct the variables you need. Survey data is messy and is a far cry from the perfect data sets econometrics students are presented with in their classes, and this is the best source to learn how to collect, construct, and analyze survey data.

James Hamilton, Time Series Analysis. I’m not really a time series guy. The closest I’ve ever been to one was in my 2015 article on food prices and food riots. Before that, it was when I was doing my Masters in Montreal and took a PhD-level course in time series analysis, for which this book served as the reference text. This thick, heavy book pretty much has all you need to know about time series. If you ever wanted to learn the ins and outs of forecasting time series data, this is the Bible.

Cheng Hsiao, Analysis of Panel Data. When I was doing my Masters, I was fortunate enough to take a course on microeconometrics, where we learned about discrete-choice models, hazard models and duration data, and panel data. This was the recommended text for the panel-data part of the course. It contains much more than most applied economists need to know, but it is a valuable reference nevertheless.

Peter Kennedy, A Guide to Econometrics. This is hands down my favorite econometrics book. Kennedy splits each chapter in three: First, a big-picture view without any technique. Second, a technical appendix with all the nitty gritty. Third, an appendix with historical details and anecdotes for those who want to know more. This book is what taught me that econometrics was as much art as it was science, and that it could be taught in an intuitive way. I refer to it very often.

Tony Lancaster, The Econometric Analysis of Transition Data. This was the reference text for the hazard models and duration data part of the microeconometrics class I took during my Masters. It’s getting a bit old, but no matter–it’s still a good introduction to the topic, which can be supplemented with Nick Keefer’s 1988 JEL article on the topic.

G.S. Maddala, Limited-Dependent and Qualitative Variables in Econometrics. This was the reference text for the discrete-choice models part of the microeconometrics class I took during my Masters, and it remains one of my favorite econometrics books. I remember the sense of wonderment I felt when I learned that you could analyze qualitative data using econometrics, and how powerful the tools in this book were. Probit, logit, all kinds of tobit, etc.–this book covers those topics in a nice way.

Stephen L. Morgan and Christopher Winship, Counterfactuals and Causal Inference: Methods and Principles for Social Research. This was what I read to get the technical know-how and details related to the potential outcomes framework. Morgan and Winship also have a very nice discussion of regression methods vs. matching methods, which few people seem to understand.

Judea Pearl, Causality. I wish I could say I read the whole thing, which is fairly heavy on the technical details. But the appendix, which includes the slides of a talk Pearl gave for the entirety of the UCLA community as an introduction to his research, taught me a lot about causality, and it seriously changed my thinking.

Jeff Wooldridge, Econometric Analysis of Cross Section and Panel Data. Perhaps the ultimate reference text for the methods used in applied microeconomics (and more). I remember when I bought my copy in 2002, and how I thought that this book was going to be a game changer after looking at the table of contents and leafing through it. Wooldridge writes very clearly and explains everything very well–more concise than Griffiths et al. (the first econometrics textbook I ever used), a bit more user-friendly than Greene (seen by the older generation as the classic).

No related content found.