Tuesday, October 9, 2018

The Refereeing Process in Economics Journals

The peer-review process is an essential part of academic publishing. We use it in the hope of ensuring the honesty, novelty, importance, and timeliness of published research. The selection of (usually anonymous) referees by a representative of the journal to which a research paper has been submitted for consideration, and the preparation of the reports/reviews by those referees, are key steps in the overall process of the dissemination of research results.

There are several different "models" when it comes to the refereeing, or peer-review process. Some of these have been described and compared recently, and in detail, here. It's also interesting to note that peer-reviewing is actually a relatively recent phenomenon in most academic disciplines.

There's no doubt that a well-crafted referee's report is a blessing - to both the recipient author and the handling Editor/Associate Editor/Editorial Board member who's looking to that report for an informed basis for making an editorial decision.

Unfortunately, such reports are not necessarily the norm in Economics/Econometrics - more on this below!

I know this is so, all too well - not only from the times when, as an author, I've been "on the receiving end" of some decidedly unhelpful reports; but also (and much more importantly) from my experiences on the other side of the fence, as a "handling editor" for a quite a number of economics, econometrics, and statistics journals.

Some would say that the academic publishing process is a bit of a crap-shoot. At times, I think that there's some truth to that. However, there's a great deal that both authors and referees can do to make the exercise more rational. 

Wednesday, October 3, 2018

A Shout-Out for The Replication Network

In May 2015 I posted about the newly-formed The Replication Network (TRN). Since then, their team has been extremely busy promoting and fostering their objectives to serve "...... as a channel of communication to (i) update scholars about the state of replications in economics, and (ii) establish a network for the sharing  of information and ideas." TRN's "..... goal is to encourage economists and their journals to publish replications."

And they're doing a great job!

As a member of TRN I receive email newsletters from them regularly. I thought I'd share the one that I received this morning, in the hope that it might encourage some of you to become TRN members.

Here it is:

Monday, October 1, 2018

Essential Fall Reading

  • Buono, D., G. Kapetanios, M. Marcellino, G. Mazzi, & F. Papailias, 2018. Big data econometrics - Now casting and early estimates. Working paper N. 82, Baffi Carefin Centre for Applied Research on International Markets, Banking, Finance, and Regulation, Bocconi University.
  • Fair, R. C., 2018. Information content of DSGE forecasts. Mimeo
  • Lewbel, A., 2018. The identification zoo - Meanings of Identification. Forthcoming, Journal of Economic Literature.
  • Pretis, F., J. J. Reade, & G. Sucarrat, 2018. Automated general-to-specific (GETS) regression modeling and indicator saturation for outliers and structural breaks. Journal of Statistical Software, 86, 3.
  • Woodruff, R. S., 1971. A simple method for approximating the variance of a complicated estimate. Journal of the American Statistical Association, 66, 411-414.
  • Zhang, R. & N. H. Chan, 2018. Portmanteau-type tests for unit-root and cointegration. Journal of Econometrics, in press.
© 2018, David E. Giles

Thursday, September 20, 2018

Controlling My Heating Bill Using Bayesian Model Averaging

Where we live, in rural Ontario, we're not connected to "natural gas". Our home furnace runs on propane, and a local supplier sends a tanker to refill our propane tanks on a regular basis during the colder months.

Earlier this month we had to make a decision regarding our contract with the propane retailer. Should we opt for a delivery price that can vary, up or down, throughout the coming fall and winter; or should we "lock in" at a fixed delivery price for the period from October to May of next year?

Now, I must confess that my knowledge of the propane industry is slight, to say the least. I decided that a basic analysis of the historical propane price data might provide some insights to assist in making this decision. It also occurred to me, after doing this, that the analysis that I went through might be of interest to readers, as a simple exercise in forecasting using Bayesian model averaging.

Here are the details...........

Sunday, September 2, 2018

September Reading List

This month's list of recommended reading includes an old piece by Milton Friedman that you may find interesting:
  • Broman, K. W. & K. H. Woo, 2017. Data organization in spreadsheets. American Statistician, 72, 2-10.
  • Friedman, M., 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, 32, 675-701.
  • Goetz, T. & A. Hecq, 2018. Granger causality testing in mixed-frequency VARs with (possibly) cointegrated processes. MPRA Paper No. 87746.
  • Güriş, B., 2018. A new nonlinear unit root test with Fourier function. Communications in Statistics - Simulation and Computation, in press.
  • Honoré, B. E. & L. Hu, 2017. Poor (Wo)man's bootstrap. Econometrica, 85, 1277-1301. (Discussion paper version.)
  • Peng, R. D., 2018. Advanced Statistical Computing. Electronic resource.
© 2018, David E. Giles

Sunday, August 5, 2018

An Archival History of the Econometric Society

For those of you who have an interest in the history of Econometrics as a discipline - that's all of you, right (?) - there's a fascinating collection of material available at The Econometric Society: An Archival History.

As the name suggests, this repository relates to the Econometric Society and the journal Econometrica. It contains all sorts of fascinating facts, correspondence, and the like.

© 2018, David E. Giles

Wednesday, August 1, 2018

Recommended Reading

Here's my reading list for August:

  • Ardia, D., K. Bluteau, & L. F. Hoogerheide, 2018. Methods for computing numerical standard errors: Review and application to value-at-risk estimation. Journal of Time Series Econometrics. Available online.
  • Bauer, D. & A. Maynard, 2012. Persistence-robust surplus-lag Granger causality testing. Journal of Econometrics, 169. 293-300.
  • David, H. A., 2009. A historical note on zero correlation and independence. American Statistician, 63, 185-186.
  • Fisher, T. J. & M. W. Robbins, 2018. A cheap trick to improve the power of a conservative hypothesis tests. American Statistician. Available online.
  • Granger, C. W. J., 2012. Useful conclusions from surprising results. Journal of Econometrics, 169, 142-146.
  • Harville, D. A., 2014. The need for more emphasis on prediction: A 'nondenominational' model-based approach (with discussion). American Statistician, 68, 71-92.
© 2018, David E. Giles

Sunday, July 15, 2018

Handbook of Quantile Regression

Quantile regression is a powerful and flexible technique that is widely used by econometricians and other applied statisticians. In modern terms we tend to date it back to the classic paper by Koenker and Bassett (1978).

Recently, I reviewed the Handbook of Quantile Regression. This edited volume comprises a number of important, original, contributions to the quantile regression literature. The various chapters cover a wide range of topics that extend the basic quantile regression set-up.

You can read my review of this book (Giles, 2018), here. I hope that it motivates you to explore this topic further.

References
Giles, D. E., 2018. Review of Handbook of Quantile Regression. Statistical Papers, 59, 849-850. 

Koenker, R., 2005. Quantile Regression. Cambridge University Press, Cambridge.

Koenker, R. and G. W. Bassett, 1978. Regression quantiles. Econometrica, 46, 33-50.

Koenker, R., V. Chernozhukov, H. Huming, & L. Peng (eds.), 2017. Handbook of Quantile Regression. Chapman & Hall/CRC, Boca Raton, FL.

© 2018, David E. Giles

Saturday, July 14, 2018

What's in a Journal Name?

Back in 2011 I put together a very light-hearted working paper titled, What's in a (Journal) Name? Here's the associated link.

That paper addressed the (obviously) important question: "Is there a a correlation between the ranking of an economics journal and the length of the journal's title?"

I analyzed a sample of 159 academic economics journals. Although there was no significant association between journal quality and journal title length for the full sample of data, I did find that there was a significant “bathtub” relationship between these variables when the data were subjected to a rank correlation analysis over sub-samples. 

This led me to conclude (p.5),among other things:
'This “bathtub” relationship will undoubtedly sound alarm bells in the corridors of publishing houses as they assess proposals for new economics journals. The title, Economics, is no longer available, having been cunningly snapped up in recent years by an open-access, open-assessment e-journal which managed to “cover all of the bases” in one fell swoop. Even more recently the American Economics Association laid claim to the titles Macroeconomics and Microeconomics, albeit with an “AEA” prefix that they may wish to re-consider. The publishers of the journal, SERIEs: Journal of the Spanish Economic Association, which was launched in 2010, will no doubt ponder the merits of dropping the last six words of its title. However, there is hope. The title Econometrica has been spoken for since 1933, but to the best of our knowledge the more worldly journal title Econometrics is still available. Publishers should register their interest forthwith!'
As usual the latter remark proved to be safe advice on my part! I wonder if my subsequent invitation to join the Editorial Board of Econometrics was some sort of reward? 

I'll probably never know!

© 2018, David E. Giles

Friday, July 13, 2018

More on Regression Coefficient Interpretation

I get a lot of direct email requests from people wanting help/guidance/advice of various sorts about some aspect of econometrics or other. I like being able to help when I can, but these requests can lead to some pitfalls -  for both of us.

More on that in a moment. Meantime, today I got a question from a Ph.D student, "J", which was essentially the following:

" Suppose I have the following regression model

             log(yi) = α + βXi + εi    ;  i = 1, 2, ...., n .

How do interpret the (estimated) value of β?"

I think most of you will know that the answer is:

"If X changes by one unit, then y changes by (100*β)%".

If you didn't know this, then some trivial partial differentiation will confirm it. And after all, isn't partial differentiation something that grad. students in ECON should be good at?

Specifically,

      β = [∂log(yi) / ∂Xi] = [∂logyi / ∂yi][∂yi∂Xi] = [∂yi  ∂Xi] / yi,

which is the proportional change in y for a unit change in X. Multiplying by 100 puts the answer into percentage terms.

So, I responded to "J" accordingly.

So far, so good.

But then I got a response:

"Actually, my model includes an interaction term, and really it looks like this:

    log(yi) = α + βXi + γ [XiΔlog(Zi)] + εi    ;  i = 1, 2, ...., n.

How do I interpret β?"

Whoa! That's not the question that was first asked - and now my previous answer (given in good faith) is totally wrong! 

Let's do some partial differentiation again, with this full model. We still have:

[∂log(yi) / ∂Xi] = [∂logyi / ∂yi][∂yi / ∂Xi] = [∂yi  ∂Xi] / yi.

However, this expression now equals [β γ Δlog(Zi)].

So, a one unit change in X leads to a percentage change in y that's equal to 100*[β γ Δlog(Zi)]%.

This percentage change is no longer constant - it varies as Z takes on different sample values. If you wanted to report a single value you could evaluate the expression using the estimates for β and γ, and either the sample average, or sample median, value for Δlog(Z).

This illustrates one of the difficulties that I face sometimes. I try to respond to a question, but I really don't know if the question being asked is the appropriate one; or if it's been taken out of context; or if the information I'm given is complete or not.

If you're a grad. student, then discussing your question in person with your supervisor should be your first step!

© 2018, David E. Giles