New Year Reading Suggestions for 2019

With a new year upon us, it's time to keep up with new developments -

• Basu, D., 2018. Can we determine the direction of omitted variable bias of OLS estimators? Working Paper 2018-16, Department of Economics, University of Massachusetts, Amherst.
• Jiang, B., Y. Lu, & J. Y. Park, 2018. Testing for stationarity at high frequency. Working Paper 2018-9, Department of Economics, University of Sydney. 
• Psaradakis, Z. & M. Vavra, 2018. Normality tests for dependent data: Large-sample and bootstrap approaches. Communications in Statistics - Simulation and Computation, online.
• Spanos, A., 2018. Near-collinearity in linear regression revisited: The numerical vs. the statistical perspective. Communications in Statistics - Theory and Methods, online.
• Thorsrud, L. A., 2018. Words are the new numbers: A newsy coincident index of the business cycle. Journal of Business Economics and Statistics, online. (Working Paper version.)
• Zhang, J., 2018. The mean relative entropy: An invariant measure of estimation error. American Statistician, online.

© 2019, David E. Giles

December Reading for Econometricians

My suggestions for papers to read during December:

• Askanazi, R., F. X. Diebold, F. Schorfheide, & M. Shin, 2018. On the comparison of interval forecasts. PIER Working Paper 18-013, Penn. Institute for Economic Research, University of Pennsylvania.
• Meintanis, S. G., J. Ngatchou-Wandji, & J. Allison, 2018. Testing for serial independence in vector autoregressive models, Statistical Papers, 59, 1379-1410.
• Milas, C., T. Panagiotidis, & T. Dergiades, 2018. Twitter versus traditional news media: Evidence for the sovereign bond markets. Mimeo., Management School, University of Liverpool.
• Ollech, D., 2018. Seasonal adjustment of daily time series. Discussion Paper No. 41/2018, Deutsche Bundesbank.
• Sickles, R. C., W. Song, & V. Zelenyuk, 2018. Econometric analysis of productivity: Theory and implementation in R. Working Paper WP08/2018, Centre for Efficiency and productivity Analysis, Department of Economics, University of Queensland.
• Skeels, C. L. & F. Windmeijer, 2018. On the Stock-Yogo tables. Econometrics, 6 (4), 44.

© 2018, David E. Giles

More Long-Run Canadian Economic Data

I was delighted to hear recently from former grad. student, Ryan Macdonald, who has worked at Statistics Canada for some years now. Ryan has been kind enough to draw my attention to all sorts of interesting items from time to time (e.g., see my earlier posts, here and here).

I always appreciate hearing from him.

His latest email was prompted by my post, A New Canadian macroeconomic Database.

Ryan wrote:

"I saw your post on long run data and thought you might be interested in a couple of other long-run datasets for your research.  If I remember correctly you are familiar with the GDP/GNI series, Long-run Real Income Estimates. I also added the long-run Bank of Canada commodity price series that go back to 1870 to it.  There is also a dataset for the provinces with estimates going back to 1950 or 1926 depending on the variable: Long-run Provincial and Territorial Data ."

Thanks for this information, Ryan.This will be very helpful, and I'd be more than happy to publicize any further such developments.

© 2018, David E. Giles

A New Canadian Macroeconomic Database

Anyone who's undertaken empirical macroeconomic research relating to Canada will know that there are some serious data challenges that have to be surmounted.

In particular, getting access to long-term, continuous, time series isn't as easy as you might expect.

Statistics Canada has been criticized frequently over the years by researchers who find that crucial economic series are suddenly "discontinued", or are re-defined in ways that make it extremely difficult to splice the pieces together into one meaningful time-series.

In recognition of these issues, a number of efforts have been made to provide Canadian economic data in forms that researchers need. These include, for instance, Boivin et al. (2010), Bedock and Stevanovic (2107), and Stephen Gordon's on-going "Project Link".

Thanks to Olivier Fortin-Gagnon, Maxime Leroux, Dalibor Stevanovic, &and Stéphane Suprenant we now have an impressive addition to the available long-term Canadian time-series data. Their 2018 working paper, "A Large Canadian Database for Macroeconomic Analysis", discusses their new database and illustrates its usefulness in a variety of ways.

Here's the abstract:

"This paper describes a large-scale Canadian macroeconomic database in monthly frequency. The dataset contains hundreds of Canadian and provincial economic indicators observed from 1981. It is designed to be updated regularly through (the) StatCan database and is publicly available. It relieves users to deal with data changes and methodological revisions. We show five useful features of the dataset for macroeconomic research. First, the factor structure explains a sizeable part of variation in Canadian and provincial aggregate series. Second, the dataset is useful to capture turning points of the Canadian business cycle. Third, the dataset has substantial predictive power when forecasting key macroeconomic indicators. Fourth, the panel can be used to construct measures of macroeconomic uncertainty. Fifth, the dataset can serve for structural analysis through the factor-augmented VAR model."

Note - these are monthly data! And they're freely available. Although the paper doesn't appear to provide the source for accessing the data, Dalibor kindly pointed out to me that there's a download link here, on his webpage. This link will give you the data in spreadsheet form, together with all of the necessary background information.

The only slight concern that I have about this resource - and I don't want to sound ungrateful - is the issue of the updating of the data over time. You'll note from the abstract that the database "...... is designed to be updated regularly through (the) StatCan database....". Given my comments (above) about some of the issues that we've all faced for a very long time when it comes to StatCan data, I  know that updating this new database on a regular basis is going to be a bit of a challenge.

Added 8 March 2019: I'm glad to learn that new update of the database is now available here.

However, let's not let this concern detract from the considerable benefits that we'll all derive from having access to this rich set of Canadian macroeconomic time-series.

Thanks, again, to the authors for constructing this database, and for making it freely available!

References

Bedock, N. & D. Stevanovic, 2017. An empirical study of credit shock transmission in a small open economy. Canadian Journal of Economics, 50, 541–570.

Boivin, J., M. Giannoni, & D. Stevanovic, 2010. Monetary transmission in a small open economy: more data, fewer puzzles. Technical report, Columbia Business School, Columbia University.

Fortin-Gagnon, O., M. Leroux, D. Stevanovic, & S. Suprenant, 2018. A large Canadian database for macroeconomic analysis. CIRANO Working Paper 2018s-25.

Gordon, S., 2018. Project Link - Piecing together Canadian economic history. Département d'économique, Université Laval.

© 2018, David E. Giles

More Sandwiches, Anyone?

Consider this my Good Deed for the Day!

A re-tweet from a colleague whom I follow on Twitter brought an important paper to my attention. I thought I'd share it more widely.

The paper is titled, "Small-sample methods for cluster-robust variance estimation and hypothesis testing in fixed effect models", by James Pustejovski (@jepusto) and Beth Tipton (@stats-tipton). It appears in The Journal of Business and Economic Statistics.  

You can tell right away, from its title, that this paper is going to be a must-read for empirical economists. And note the words, "Small-sample" in the title - that sounds interesting.

 Here's a compilation of Beth's six tweets:

Econometrics Reading for November

In between raking leaves and dealing with some early snow, I've put together this list of suggested reading for you:

• Beckert, W., 2018. A note on specification testing in some structural regression models. Mimeo., Department of Economics, Mathematics and Statistics, Birkbeck College, University of London.
• Clarke, D., 2018. A convenient omitted bias formula for treatment effect models. Economics Letters, in press.
• Liu, Y. & Y. Rho, 2018. On the choice of instruments in mixed frequency specification tests. Mimeo., School of Business and Economics, Michigan Technological University.
• Lütkepohl, H., A. Staszewska-Bystrova, & P. Winker, 2018. Constructing joint confidence bands for impulse functions of VAR models - A review. Lodz Economic Working Paper 4/2018, Faculty of Economics and Sociology, University of Lodz.
• Richardson, A., T. van Florenstein Mulder, & T. Vehbi, 2018. Nowcasting New Zealand GDP using machine learning algorithms.
• Słoczyński, T., 2018. A general weighted average representation of the ordinary and two-stage least squares estimands. Mimeo., Department of Economics, Brandeis University.

© 2018, David E. Giles

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. 

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:

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

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...........