Adapting Econometrics Courses

Suggestions for incremental change to econometrics courses, drawn from the ten building blocks of Economy Studies.

General approach

Change often happens incrementally and slowly. In the economics textbook market, for example, there is an unwritten rule that new textbooks cannot differ more than roughly 15% from the standard textbook in order to be ‘acceptable’ (Colander, 2003).

While our book clearly breaks this rule and proposes more far-reaching and fundamental changes in most chapters, in this chapter we focus instead on how existing courses could be adjusted incrementally. By doing so, we hope to assist educators in improving and adapting the courses they teach without needing to rip them up and start again, as well as helping students make suggestions for how this could be done.

First, we set out the typical contents of current courses. Second, we provide our suggested additions and changes. It is important to note that we pose all these suggestions as potential sources of inspiration, not a checklist of all the things that necessarily should be included. After all, there is a practical limit to what can be taught within a single course.


Typical contents of current courses

Large parts of economics programs are currently devoted to econometrics, mathematics and statistics. More specifically, students are taught calculus, algebra, optimization and, most important of all, regression analysis. In this way, students learn how to work with mathematical models and test them with the help of existing quantitative datasets.  

Frequently used textbooks:

  • Introduction to Econometrics by James H. Stock and Mark W. Watson
  • Introductory Econometrics: A Modern Approach by Jeffrey Wooldridge
  • Econometric Analysis by William Greene
  • A Guide to Modern Econometrics by Marno Verbeek
  • Using Econometrics by A. H. Studenmund
  • Basic Econometrics by Damodar N. Gujarati
  • Introduction to Econometrics by Christopher Dougherty
  • An Introduction to Econometrics: A Self-Contained Approach by Frank Westhoff

Suggested additions and changes

Practical skills and real-world knowledge

For didactical reasons, it can be helpful to (first) work with made-up data, so that students can learn all the technical aspects step by step. In their future career, students will, however, have to work with real data and be able to draw substantive, and not only technical, conclusions. For this reason, we advise to let students work as much as possible with real data and substantive questions, so that they learn how to apply technical skills and knowledge. 

This brings us to a larger point that the focus of methods courses should not be on (advanced and fancy) techniques, but on those skills and knowledge that are needed in practice. The vast majority of students will become professional economists tasked with practical, rather than academic, issues and problems. This is not to say that they do not need to learn how to perform methods technically correct, they definitely do. But it does mean that sophisticated techniques are often unnecessary and potentially even harmful as they are more likely to be applied or interpreted wrongly. Rather than spending teaching time on sophisticated techniques, we thus advise to spend more time on practically applying methods and carefully interpreting the substantive implications of results. After learning about the technicalities of a method, students can be given assignments and projects in which they need to apply the method to a real world issue. Some courses already do this. It is important to note here that the goal in such cases is to gain insight in how to tackle a practical problem and not to test a theoretical model, although theories of course can be helpful.

For more detail, see Building Block 2: Know Your Own Economy and Building Block 9: Problems & Proposals.

A range of analytical tools and approaches

Methods courses in economics could be enriched by teaching about philosophy of science, data collection and other data analysis methods, such as network analysis. 

First, philosophy of science. To have a good understanding of what these methodological choices entail, a good basis in the philosophy of science is necessary. This is not specific to economics, but applies to all scientific disciplines. The specific knowledge about philosophy of science that should be taught is, however, specific to economics as its subject matter and issues have their own characteristics. Teaching about philosophy of science does not need to be (overly) abstract and can productively be integrated with concrete applications of methods, thereby making it clear to students how these matters are relevant in practice. 

Second, data collection. In order to be able to apply a data analysis method, one needs data and how this data is collected is crucial to how the results should be interpreted. For this reason, it is critical that methods courses devote time to discussing how data is collected and teaching students data collection methods, such as survey research and experiments. By gaining hands-on experience with such data gathering methods, students will not only learn how to apply them in their later careers but also be better at critically assessing existing datasets and applications by others. 

Third, other data analysis methods. While regression analysis is a highly important data analysis method, it is not the only relevant one. For this reason, we encourage teachers to also teach other data analysis methods, such as network analysis. The importance of such newer methods is increasingly clear, with examples such as the financial crisis of 2008 and its build-up with massive international financial flows, or the covid-19 crisis of 2020 with the global spread of the virus.

For more detail, see Building Block 7: Research Methods & Philosophy of Science.

Teaching Materials

  • Economic Methodology: Understanding economics as a science by Marcel Boumans and John B. Davis, from 2010. A sharp and accessible introduction into economic methodology and philosophy of science with explanations of different views on science and key debates on how economics should be practiced.
  • Social Research Methods by Alan Bryman, most recent edition from 2015. A prominent textbook that introduces a wide variety of quantitative and qualitative research methods, such as interviews, structured and participant observation, content analysis, and survey research.
  • The SAGE Handbook of Applied Social Research Methods by Leonard Bickman and Debra J. Rog, most recent edition from 2009. A leading textbook on applied research with attention to choosing the right method for the question at hand, practical considerations, and how to make informed methodological decisions for a variety of quantitative and qualitative methods. 
  • Handbook of Research Methods and Applications in Heterodox Economics by Frederic Lee and Bruce Cronin, from 2016. An instructive collection of essays with explanations, reflections on and applications of innovative research methods that deviate from the standard econometric approach usually taught in economics programmes, such as survey research, network analysis, experiments, ethnography, and agent-based computational modelling. 
  • Qualitative Research Practice A Guide for Social Science Students and Researchers by Jane Ritchie, Jane Lewis, Carol McNaughton Nicholls, and Rachel Ormston, most recent edition from 2013. A useful introduction into how to do rigorous and reflective quantitative research with chapters on interviews, focus groups, observation, research design, ethical considerations, and data analysis.
  • Mostly harmless econometrics: An empiricist’s companion by Joshua D. Angrist and Jörn-Steffen Pischke, from 2008. This textbook aims to introduce students to econometrics in a more practical way and with more attention to causality. 
  • Handbook of Research Methods and Applications in Experimental Economics by Arthur Schram and Aljaž Ule, from 2019. This informative collection of essays discusses the various aspects of experimental economics, from field experiments and neuroeconomics to methodological procedures and its relation to theory and policy. 
  • Networks by Mark Newman, from 2010. This introductory textbook helps students understand how networks can be studied and modelled, whether one studies a social, biological or technological network. 
  • Social Network Analysis by John Scott, from 1991. This textbook introduces students to social network analysis, its history, concepts and methodology. 
  • International handbook of survey methodology by Edith, D. de Leeuw, Joop J. Hox, & Don A. Dillman, from 2008. This useful collection of essays introduces students to the various aspects of survey research, from survey design and implementation to the data analysis and ethical considerations.
  • The Handbook of Pluralist Economics Education by Jack Reardon, from 2009, chapter 10. This useful book on how to diversify economics programs, includes a chapter full of ideas and suggestions for courses on mathematics. 

Institutions and different ways of organising the economy

Some parts of the economy are more often, or easily, captured in statistics than others. Key examples of this are paid versus unpaid labour and the formal versus informal sector. These biases in data towards some parts of the economy have important implications for how different societal groups are represented. Women, for example, do a disproportionate amount of unpaid labour. Not capturing that part of the economy leads to underestimating and valuing women’s contributions to the economy. Furthermore, it can lead to missing  important societal issues and providing fitting policy advice that takes them into account. When it comes to labour market policies, for example, it is important to include unpaid labour in the analysis, as ignoring it will lead to overlooking issues related to the double burden, or second shift, that women disproportionately perform. Similar biases can be observed surrounding the informal sector, often leading to an underrepresentation of workers in the Global South. 

As a general rule, it is important to be aware of how the data is collected and not to (apriori) assume this is a ‘representative’ sample of the people and the economy. Often data contains biases towards some groups and parts of the economy, and this can hide and exacerbate existing inequalities. A good researcher is not only technically capable to work with data, but is also aware of its limitations which inform the substantive conclusions that can be drawn from them.

Teaching Materials

  • Invisible Women: Data Bias in a World Designed for Men by Caroline Criado-Perez, from 2019. This influential bestseller draws attention to the ways in which data are biased in terms of gender, with chapters devoted to statistics on work, health, fashion, and people’s public and daily lives.
  • Data Feminism by Catherine D’Ignazio and Lauren F. Klein, from 2020. This book argues data are mainly constructed by and also often biased in favour of white men and argues a intersectional feminist approach can help our understanding of the world and improve data science.
  • The perils of perpetuating postcolonial biases in research by Munyaradzi Makoni, from 2018. This article briefly discusses Western biases in research and provides short suggestions on how to tackle them.

Societal relevance and normative aspects

The goal of many research methods is to provide knowledge about the world, which is as objective as possible. Often, methodological decisions do, however, have ethical implications. For this reason, it is important that students learn to think about the normative aspects of conducting research, so that they can make carefully considered decisions about it in their future careers.

For more detail, see Building Block 1: Introducing the Economy and Building Block 10: Economics for a Better World.

Teaching Materials

  • The Oxford Handbook of Professional Economic Ethics by George F. DeMartino and Deirdre McCloskey, from 2016, chapters 18 and 19. This insightful collection of essays explores the different aspects of ethics in economics, with two chapters devoted to statistical significance, and honesty and integrity in econometrics. 
  • The Oxford Handbook of Philosophy of Economics by Harold Kincaid and Don Ross, from 2009. This collection of essays provides an overview of the literature on the philosophy of economics, divided up in sections on microeconomics, macroeconomics and welfare, with chapters on experiments, computational economics, causality, data mining and facts and values in modern economics.
  • Philosophy of Economics by Uskali Mäki, from 2012. This collection of essays introduces students to the various ideas and debates surrounding the philosophical foundation of economics and its methods, with chapters on econometrics, game theory, experiments, economic forecasting, and mathematics.  


When teaching students various research methods, it can be enriching to briefly discuss their origins and development. Teaching students about the context and debates surrounding research methods can get students more engaged with the material as it involves a different kind of knowledge, which is more substantive than technical. Teaching about the history of methods can also be combined or integrated with teaching students about philosophy of science, as the two developed in interaction with each other.

For more detail, see Building Block 3: Economic History, Building Block 4: History of Economic Thought & Methods and Building Block 7: Research Methods & Philosophy of Science.

Teaching Materials

  • Economic Methodology: A Historical Introduction by Harro Maas, from 2014. A well-written and useful book on the history of economic methodology from debates about deduction and induction, statistics, modelling, and experiments in economics.
  • Economic Methodology: Understanding economics as a science by Marcel Boumans and John B. Davis, from 2010. A sharp and accessible introduction into economic methodology and philosophy of science with explanations of different views on science and key debates on how economics should be practiced.

What to take out

To create space for the above suggested additions, we advise to focus more on the core statistical techniques and methods and less on sophisticated mathematical and statistical skills, as surveys among employers of economists indicate that professional economists rarely need them. While in elective courses it can be valuable to focus on sophisticated econometric techniques, in compulsory methods courses it is more useful to focus on mastering applying and communicating relatively basic statistical techniques.