Teaching
Nick Weaver has been using real life economic time series data in his undergraduate economics teaching since the start of UK Data Service back in 2003. The popular Applied Economics module uses the time series data from the Office of National Statistics hosted by ESDS International (now part of the UK Data Service) to explore the evidence and trends underlying the current state of the economy.
“I think economists ought to be doing much more looking at the data in the real world rather than reading what other people have done” he argues. He goes on to argue “That’s what people should be doing economics for – not just to reproduce what other people’s work but to understand the UK economy itself.”
The aim of the Applied Economics module is to introduce students to how economic data can be used to analyse current policy topics in public economics. Specifically, students are introduced to econometrics, macroeconomics and applied data via learning to estimate a consumption function for the UK. They are shown various types of data sources, software tools and applications, types of variable, the structure of data, simple statistical techniques and economic theories. The module demonstrates how the multiple regression model, which is further developed in basic econometrics units in the second year, is the work-horse technique of applied economics. Underlying the practical work is the principle of replicability for published research in the economics discipline. Because the lack of such replicability has led some to criticise economics as a science, students are taught in a way that incorporates this as a baseline from the first year.
Students come into the BA Economic Science degree with A-Level Mathematics, ready to start playing with data, statistics and equations. Data from the UK Dat Service are used in a practical exercise within the Applied Economics module, which is held in the second semester. In this module they get to do some applied economics research, in a very structured, hand-held manner, without really knowing much about econometrics. They are introduced to things that they will learn in-depth later on, with the aim of showing them that they can produce something that looks good as the kind of project they might be able to see being asked for in the workplace. This, along with familiarising them with econometrics software packages, and refreshing their understanding of tasks such as matrices and matrix inversion in Excel, helps spark their interest and build confidence early on.
Students are given a lecture introducing the particular problem of estimating a consumption function for the UK, with an explanation of the relevant statistical techniques and economic theory. This is accompanied by a chapter to read from Backhouse, Roger E. (1991) Applied UK Macroeconomics, Basil Blackwell, Oxford (see Part II The demand side, Consumption and Savings) in which he estimates the consumption function, going through a series of different equations and different models, which become increasingly more elaborate. Backhouse estimates each of these in turn, explaining along the way why each one turned out as it did, and what the problems were.
In IT lab tutorials, students are then shown, by working step-by-step through the chapter, how to reproduce the model and then update it to the present using current data. The data they need for updating the model is extracted live from ESDS International, and combined with data such as house prices from other sources. Thus they are required to pull data from different sources, splice to together, combine it in a spreadsheet and update an existing consumption model.
This exercise, while gently spoon-feeding the students to some extent, is useful for showing undergraduates how data may need to be manipulated. For instance, the house price data series does not start at the same period as the other data, but there is another series that can be tagged onto it to create a consistent series, with some manipulation at the overlap of the two series to make them consistent. This also gives the students an opportunity to learn to footnote how this manipulation was done, to emphasise correct documenting methods, key for scientific replicability.
This practical assignment is one third of their final mark, the remainder of which is assessed by exam.