I have the 1988 and 2009 editions Gujarati and Porter's Basic Econometrics in front of me. Chapter 1 has been updated for the 21st century:
The Internet has literally revolutionized data gathering. If you just "surf the net" with a keyword (e.g. exchange rates), you will be swamped with all kinds of data sources. In Appendix E we provide some of the frequently visited websites...You may want to bookmark the various websites that might provide you with useful economic data.
Seriously? Today's undergrads don't need to be told how to surf the net; most don't remember a world without it.
It's easy (and fun) to mock well-intentioned advice. Yet the authors have a point: greater data availability, together with advances in computing power and statistical software, is transforming both economics and econometrics. My question is: what are the implications of this transformation for undergraduate teaching?
Consequently, the 1988 and 2009 editions of Basic Econometrics have the same basic structure. The 2009 edition has a couple of added chapters (on panel data and forecasting), and updated examples. Other chapters, for example, the one covering model specification, have been expanded and updated. If you've been teaching econometrics for years, you probably think it's a good, solid textbook, that covers everything students need to know. Certainly no worse than others on the market.
This standard approach to econometrics - as represented by Gujarati and Porter, and other major textbooks - worked fine back in 1988. The costs of data and computer access, together with the unfriendliness of software, meant that few students had the courage to run regressions. But today students start running regressions in their high school data management classes. By the time they are advanced undergraduates, most are capable of doing some serious econometrics.
Since today's students can realistically be expected to actually do econometrics, they need to learn about research methods. For example, the methodology of econometrics, as described by Gujarati and Porter, boils down to 8 steps, the first of which is "Statement of theory or hypothesis." But how does a student go about coming up with a hypothesis? The econometricians leave theory to the theorists, the theorists leave hypothesis testing to the econometricians, and the students are caught in the middle. Type "econometrics course outline" into a search engine, and look at the topics covered in a typical econometrics course. Formulating a research hypothesis isn't one of them.
An econometrician might (justifiably) respond: this isn't our problem. Students should be learning how to come up with research hypotheses in their other courses. Fair enough. But the practical problems students encounter when actually doing econometric work also receive little or no attention in econometrics courses.
Take, for example, the problem of missing observations. Gujarati and Porter devote about half a page of the 2009 edition of the textbook to dealing with missing observations. Their advice? If "the reasons for the missing data are independent of the available observations", then the cases with missing observations are "ignorable" and can just be dropped. Otherwise, treat as for sample selection bias.
Yet the presence of missing observations is a vital diagnostic tool. Suppose, for example, a researcher is interested in the impact of husband's income on the labour supply of mothers of young children. "Husband's income" will be missing for any unmarried mother. The researcher, therefore, needs to redefine her research question. Is she only interested in the labour supply of married women? In this case, she can drop her missing observations, and acknowledge that her findings apply to only a subset of mothers. Or is she interested in the impact of other household members' incomes more generally - including the income of cohabiting partners, for example? In this case, she might wish to recode her data to include a new explanatory variable "income of other household members", sitting it equal to zero for people who are living alone. A final strategy, especially useful if the information for husband's income is in categorical form, is model husband's income with a series of dummy variables (less than $20,000; $20,000 to $40,000 etc), and just include a dummy set equal to one for mothers with no husband present.
The point is that the presence of missing observations is a simple diagnostic test that indicates that the researcher has not yet clearly defined their sample or explanatory variables. It can't be formalized or written mathematically - but it would be pretty sad if mathematical elegance was a more important criterion than practical usefulness.
Missing observations can also arise because of the way that data is gathered (see, for example, my post on "when is a missing observation not really missing"). For example, information about savings in registered retirement savings plans might be gathered through two questions (1) "do you have a savings plan?" and, if yes, (2) "how much money is in your plan?" To record everyone who answered "no" to (1) - and thus did not answer question (2) - as "missing" is throwing away information. It seems obvious that no one would do that - but in fact it's easy to do, since software packages like Stata don't distinguish between valid skips (like this example) and refusals. And when are students ever taught how to read questionnaires and utilize data most effectively? Why is this less worthy of class time than, say, the formalities behind logit models?
When I discuss these issues with colleagues - econometricians or non-econometricians alike - they typically say "yes, I agree, what we need is a course in applied economics." It's the easiest solution to any pie division problem: make the pie bigger. No one has to change what they're doing, question the worth or relevance of the material that they're teaching - though perhaps they should (see, for example, David Giles on multicollinearity).
Once I heard someone say this about teaching, "It's not how much material you get through, it's how much material the students get through." Formalities - theorems, proofs, whatever -- are soon forgotten. Methods, ways of approaching or thinking about problems - may stick around a little longer.