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Analyzing the Last Five Years of the US Economy for an Intermediate Macro Course

The page authored by Steven A. Greenlaw, University of Mary Washington.
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This material was originally developed as part of the Carleton College Teaching Activity Collection
through its collaboration with the SERC Pedagogic Service.


Intermediate macro students are asked to compile and analyze data on the components of consumption or investment expenditures using data on variables suggested by standard macro theories. They describe each data series in a appropriate way (e.g. growth rates and turning points) and search for correlations between the dependent and explanatory variables. They discover that different theories explain different components of consumption and investment best.

Learning Goals

The objective of this assignment is for students to build a correspondence in their minds between the macroeconomic variables that comprise standard theories of consumption and investment, and the real world values and trends in those variables.

Context for Use

In my experience, there is a disconnect in the intermediate theory course between student understanding of the economy as characterized by standard macroeconomic models and faculty understanding of the same models. Students find it difficult to really grasp what abstract models are explaining. The problem isn't in the models, but rather in how students perceive them. One way to bridge this disconnect is to ask students to dig into the data for the variables the models are using, which is what this assignment asks them to do.

The assignment asks students to work in groups of three or four. Organizing this as a group project makes it feasible for class sizes as large as 50 students (or more with TA support). This assignment should be given after the underlying theoretical material has been covered in the course, that is, after the chapter on consumption expenditure or investment expenditure has been completed. The assignment can be done either by itself (either or both consumption or investment expenditures) or as part of a larger project which explores more sectors of the economy. Each assignment takes perhaps 10 minutes to introduce in class (less the second time), and 50 minutes to review afterwards.

Description and Teaching Materials

Students are told that the purpose of this example is to give them initial experience in the type of applied data analysis done by professional economists. The analysis is not very sophisticated, and that's as it should be at this level.

This example is actually two assignments: the first on consumption expenditures and the second on investment expenditures. In the first assignment, students are asked to collect quarterly data over the last five years on the components of consumption expenditure, including consumer durable, consumer nondurable and service expenditures. In the second, they do the same for the components of investment expenditure: durable equipment and software, non-residential investment, residential investment, and inventory investment expenditures,

Students must first create tables and time series charts for each variable, as well as provide complete citation information for where the data was obtained. Next they must think about how to characterized each data sample. Should they use levels or growth rates? Should they identify changes in the trend, or turning points in cyclical data? Noodling this out is where working as a group can be particularly useful.

Next, students need to think about the theoretical models they have studied regarding consumption or investment expenditure. What variables should be examined for the simple Keynesian consumption function, the Life-cycle model, or the Permanent Income Hypothesis? What variables should be examined for the simple Keynesian model of investment, the Accelerator model, or the Neoclassical cost of capital model? What are appropriate measures of interest rates, or income, or wealth? Clearly, there are a variety of choices for students to make.

Finally, how are the explanatory variables correlated with the dependent variables? Are these judgments based on formal statistical correlations or visual approximations? How are lags taken account of in the analysis? What conclusions can be drawn about the ability of the different models to explain each of the dependent variables?

The assignment handout is [here].

Teaching materials needed include:

Teaching Notes and Tips


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