In Praise of a Good Abstract

When you're writing up your research, it's a good idea to keep in mind that a lot of the potential readers of your exciting new paper are going to be busy people. I'm not talking about journal editors and referees - they're busy too, but they have an obligation to read your paper carefully. 

The rest of us have no such obligation, so you have to convince us that your research results are as interesting and important to us as they are to you.

I read a lot of papers dealing with econometrics and various areas of statistics. I also "pass over" even more papers that come my way via emails, web pages, and the like. 

Sometimes I'm following particular researchers/authors because I know from past experience that their work will be of interest to me. Otherwise, the title might catch my eye, and then I'll go as far as reading the abstract, and maybe the concluding section. Depending on the impression I've gained by that stage, I may or may not read the paper itself.

I think that, in this respect, I'm pretty typical of most of my colleagues. So, that's why the abstract of your paper is crucially important.

Now, I'm involved in enough journal editing work and peer reviewing to understand that there are often (usually) constraints on the allowable length of an abstract. This varies from journal to journal. Even without such constraints, keep in mind that an "abstract" is just that - it's a short overview of what your paper is about, and what your research contributes to our knowledge. A three-page "abstract" makes no sense!

With all of this in mind, the other day I was thinking about papers I've read that had excellent, really compelling, abstracts. 

I think that some of them are worth sharing.

One of my all-time favourites is a paper that I've referred to on a number of occasions in this blog (e.g., here, here, and here). It's the paper by Toda and Yamamoto (1995), which deals with testing for Granger non-causality using non-stationary time-series data. I remember that when I read the abstract of this paper for the very first time, I thought to myself, "Wow! This abstract tells you exactly what the contribution of the paper is, and it summarizes exactly how to implement the new results".

Here's the abstract:

"This paper shows how we can estimate VAR's formulated in levels and test general restrictions on the parameter matrices even if the processes may be integrated or cointegrated of an arbitrary order. We can apply a usual lag selection procedure to a possibly integrated or cointegrated VAR since the standard asymptotic theory is valid (as far as the order of integration of the process does not exceed the true lag length of the model). Having determined a lag length k, we then estimate a (k + dmax)th-order VAR where dmax is the maximal order of integration that we suspect might occur in the process. The coefficient matrices of the last dmax lagged vectors in the model are ignored (since these are regarded as zeros), and we can test linear or nonlinear restrictions on the first k coefficient matrices using the standard asymptotic theory."

What about one more example?

Phillips (1986) developed the theory that provided a formal explanation of the "spurious regressions" problem noted by Granger and Newbold (1974). Phillips' paper is mathematically elegant and many readers would find it challenging. However, the abstract is crystal-clear:

"This paper provides an analytical study of spurious regressions involving the levels of economic time series. As asymptotic theory is developed for regressions that relate independent random walks. It is shown that the usual t ratio significance tests do not possess limiting distributions but actually diverge as the sample size T approaches infinity. The Durbin-Watson statistic, on the other hand, converges in probability to zero. An alternative asymptotic theory is also analyzed. An alternative asymptotic theory is developed based on the concept of continuous data recording. This theory together with the large sample asymptotics that we present go a long way towards explaining the experimental results of Granger and Newbold (1974, 1977)."

Interestingly, the Granger-Newbold paper did not have an abstract!

If you have any of your own favourite abstracts, I'd be pleased to hear about them.

A Confession - When I look back at the abstracts for some of my own papers, I must admit that I'm not that good at taking my own advice!


References

Granger, C. W. J. and P. Newbold, 1974. Spurious regressions in econometrics. Journal of Econometrics, 2, 111-120.

Phillips, P. C. B., 1986. Understanding spurious regressions in econometrics. Journal of Econometrics, 33, 311-340.

Toda, H. Y. and T. Yamamoto, 1995. Statistical inferences in vector autoregressions with possibly integrated processes . Journal of Econometrics, 66, 225-250.

© 2013, David E. Giles