Tuesday, November 27, 2018

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 EstimatesI 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

Thursday, November 22, 2018

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:

Wednesday, November 14, 2018

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:

Monday, November 5, 2018

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