More on the Quality of Economic Data

Yesterday I posted two pieces relating to the quality of economic data, in general terms, and with reference to China.

I'm firmly of the view that we need to be paying more attention to data quality than we currently do as economists. We also need to keep in mind that data are frequently revised, and this has implications for policy conclusions based on preliminary figures.

To help you with your reading on this topic, here's a small selection of papers that touch on different aspects of this topic:

Now That the Semester is Over....

Another teaching term is done, and the exams are all graded!

HT to my colleague, Emma Hutchinson, for this timely item:

Bias Reduction Paper Published

Another of our papers on bias reduction for Maximum Likelihood estimators has now been published. This one is titled, "On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution", and is co-authored with Ryan Godwin and Helen Feng. It's in Vol. 42 (11) of Communications in Statistics - Theory and Methods, and is available here.

This paper stems from an ongoing research program with Helen, Ryan, and others. Other posts relating to this program can be found here and here. There's more of this on the way!

© 2013, David E. Giles

The Reliability of China's Economic Data

There have long been concerns about the reliability of published macroeconomic data for China. About 3 months ago, the U.S. - China Economic and Security Review Commission published a timely report, titled, "The Reliability of China's Economic Data - An Analysis of National Output". The report certainly makes interesting reading.

In a post earlier today I warned about the importance of data quality. When the data relate to an economy that's size and importance as that of China, then it's time to sit up and take notice!

Data Quality is Paramount

Yesterday, in a post on the Worthwhile Canadian Initiative, Frances Woolley rightly drew attention to some rather disturbing issues associated with the upcoming release of the 2011 National Household Survey (NHS), by Statistics Canada. In a nutshell, she asks the question, "How can we be sure that the NHS information about the religious beliefs of Canadians is accuarate?"

Recently, I made the comment: Data - the econometrician's lifeblood! Can't function without it." I wish I'd been more specific, and said "reliable data."

The T. D. Dwivedi Memorial Lecture

Yesterday, I was greatly honoured to present the 2nd. invited T. D. Dwivedi Memorial Lecture, in the Department of Mathematics & Statistics at Concordia University, in Montreal. The late Try Dwivedi was instrumental in the establishment of statistics at Concordia, and he also played a leading role in the development of the statistical profession in Canada generally

I'm linked to Dr. Dwivedi through the work that each of us did with V. K. (Viren) Srivastava. So, I have a "Dwivedi number" of 2.  You'll find more about this in an earlier post about Viren here.

The talk that I gave was titled, "Bas Adjustment for Nonlinear Maximum Likelihood Estimators", and you can download my slides from here if you're interested. The material for the lecture was based on a research program that I've been involved with in recent years, jointly with Helen Feng, Ryan Godwin, Jacob Schwartz, and others. A previous post on this blog discussed some of this research.

I'd like to thank the Dwivedi family, Yogen Chaubey (Department Chair), and the faculty of the Department of Mathematics and Statistics at Concordia University, for the kind invitation, their outstanding hospitality, and for a memorable visit to Montreal.

© 2013, David E. Giles

A First Encounter With Monte Carlo Simulation

In my second-year undergraduate course on Statistical Inference for economists, I use Monte Carlo simulation with EViews to illustrate the notion of the "sampling distribution" of a statistic, such as an estimator. This is hardly unusual. However, before we get started I have to persuade the students that this whole Monte Carlo thing might actually work!

So, we go through an exercise where we use simulation to approximate the value of π.

You Can Quote Me on That

The other day I came across the Empirical Quotes page on Mark Byran's blog. Some of his quotes related specifically to econometrics, and I thought I'd share a few others. That certainly doesn't mean that I agree with them all!

In Praise of Quandl!

Data - the econometrician's life-blood! Can't function without it.

So, when a new source of data becomes available - especially one that's sophisticated, reliable, and FREE - it's time to sit up and take notice. Quandl is a recent Canadian start-up that delivers economic and financial time-series data, and then some.

It's an interesting business model. When you go to Quandl, you link to the original sources of the data, you know when the data were last updated, and you get some basic graphical analysis before you download the numbers. 

If you're an R, MATLAB, ........... user, it's a breeze to import the data and get busy with the analysis. I've tried out the associated R package, and it's just great. As you can probably tell - I'm hooked!

I can see myself using Quandl a lot for teaching purposes, as well as for my research, and I suspect that my students will be equally enthusiastic.

© 2013, David E. Giles

Star Wars

Today, Ryan MacDonald, a UVic Economics grad. who works with Statistics Canada, sent me an interesting paper by Abel Brodeur et al.: "Star Wars: The Empirics Strike Back". Who can resist a title like that!

The "stars" that are being referred to in the title are those single, double (triple!) asterisks that authors just love to put against the parameter estimates in their tables of results, to signal statistical significance at the 10%, 5% (1%!) levels. A table without stars is like champagne without bubbles!