Using Spreadsheets to Conduct Monte Carlo Experiments for Teaching Introductory Econometrics

Abstract

1. Introduction In recent years a number of authors have begun to use Monte Carlo simulation to help explain elementary concepts in statistics and econometrics (e.g., Gujarati 1999, pp. 79-81 and 159-161; Albright, Winston, and Zappe 2000, pp. 191-194; Studenmund 2001, pp. 101-102). Furthermore, Kennedy (1998a, b) has argued that the Monte Carlo experiment is an indispensable pedagogical tool for the undergraduate econometrics course. In particular, he believes that Monte Carlo methods can be used to illuminate the idea of a sampling distribution, a fundamental concept that is often difficult for students to grasp. Kennedy maintains that because the notion of a sampling distribution is the lens that makes other statistical concepts clear, and because Monte Carlo methods provide a superior vehicle for acquiring this lens, introductory students should be required to describe a Monte Carlo experiment related to every major topic in the (Kennedy 1998b, p. 148). Kennedy further contends, however, that introductory students should not be asked to conduct their own Monte Carlo experiments because of the high opportunity cost of learning to program standard econometric software. I concur with Kennedy's assertion that teaching statistical concepts in the context of a Monte Carlo study is an enormously valuable pedagogical technique. I also agree that the marginal benefit derived from requiring students to program Monte Carlo experiments with standard econometric software, rather than to only demonstrate their ability to conceptualize the experiment, probably exceeds the marginal cost for most undergraduates. Nevertheless, it is clear that students can derive substantial Teaming benefits from conducting their own Monte Carlo experiments. It is well known that many students learn best through experiencing and experimenting, and even students with strong abstract reasoning abilities usually find experiential learning exercises helpful. The process of developing and experimenting with a simulation model can provide such experiential learning opportunities with otherwise very abstract statistical concepts; but the opportunity cost is a problem. I have found that the benefit can substantially exceed the cost when introductory students use spreadsheets to perform their own Monte Carlo experiments. As I hope to demonstrate below, the opportunity cost of learning associated with conducting a Monte Carlo experiment with a spreadsheet can be relatively low. Nearly all of my econometrics students juniors and seniors in finance and economics, most of whom have had a prior course in spreadsheet modeling) are familiar with spreadsheets and find the environment very natural. Thus, they learn the necessary spreadsheet commands quickly and are then able to focus on the purpose of the exercise rather than on programming. In addition, instructors can control the cost to some degree by supplying spreadsheet templates and directions tailored to the background of the students and the goals of the exercise (see Cahill and Kosicki 2001 for a useful discussion of this point). The use of spreadsheets to conduct Monte Carlo experiments in introductory courses can therefore overcome the high-opportunity-cost problem to a large extent. The use of spreadsheets to teach Monte Carlo simulation offers additional learning benefits because the environment is especially intuitive and user friendly. For instance, the spreadsheet setting invites exploration: Once a model is created, the effects of changing a parameter value can be investigated by simply entering a new number in a cell and pressing a key. Spreadsheet models can also be superior teaching tools because of the way data are displayed. Realizations of random events can be organized, annotated, and presented in a tabular form that undergraduates can easily relate to. With a well-designed spreadsheet model, students can almost see samples being drawn and estimates being created. …