Econometric Society World Congress

The Econometric Society holds a World Congress every five years. Right now, the 2015 Congress is taking place in Montréal, Canada.

Here's the full program. Enjoy!

© 2015, David E. Giles

Classic Data Visualizations

My thanks to Veronica Johnson at Investech.com for drawing my attention a recent piece of theirs relating to Classic Data Visualizations.

As they say:

"A single data visualization graphic can be priceless. It can save you hours of research. They’re easy to read, interpret, and, if based on the right sources, accurate, as well.  And with the highly social nature of the web, the data can be lighthearted, fun and presented in so many different ways. 

What’s most striking about data visualizations though is that they aren’t as modern a concept as we tend to think they are. 

In fact, they go back to more than 2,500 years—before computers and tools for easy visual representation of data even existed."

Here are the eleven graphics that they highlight:

Symmetry and Skewness

After taking your first introductory course in statistics you probably agreed wholeheartedly with the following statement:

"A statistical distribution is symmetric if and only if it is not skewed."

After all, isn't that how we define "skewness"?

In fact, that statement is incorrect. There are distributions which have a skewness coefficient of zero, but are asymmetric.

Before considering some examples of this phenomenon, let's take a closer look at the meaning of "skewness" in the statistical context.

The H-P Filter and Unit Roots

The Hodrick-Prescott (H-P) filter is widely used for trend removal in economic time-series, and as a basis for business cycle analysis, etc. I've posted about the H-P filter before (e.g., here).

There's a widespread belief that application of the H-P filter will not only isolate the deterministic trend in a series, but it will also remove stochastic trends - i.e., unit roots. For instance, you'll often hear that if the H-P filter is applied to quarterly data, the filtered series will be stationary, even if the original series is integrated of order up to 4.

Is this really the case?

Let's take a look at two classic papers relating to this topic, and a very recent one that provides a bit of an upset.

Estimating Elasticities, All Over Again

I had some interesting email from Andrew a while back to do with computing elasticities from log-log regression models, and some related issues.

In his first email, Andrew commented:

"I am interested in the elasticity of H with respect to W, e.g., hours with respect to wages. For simplicity, assume that W is randomly assigned, and that the elasticity is identical for everyone.

Standard practice would be to regress log(H) on a constant and log(W). The coefficient on log(W) then seems to be the elasticity, as it estimates d log(H) / d log(W).

But changes in log( ) are only equal to changes in percent in the limit as the changes go to zero. In practice, one typically uses discrete data. Because the changes in W may be large, the resulting coefficient is just a first order approximation of the elasticity, and is not identical to the true elasticity."

Let's focus on the third paragraph. Keep in mind that log( ), here, refers to "natural" (base 'e') logarithms.

Andrew is quite correct, and this is something that we often overlook when teaching econometrics, or when interpreting someone's regression results. I sometimes refer students to this useful piece by Kenneth Benoit. Here's a key extract from p.4:

August reading

Here's my (slightly delayed) August reading list:

• Ahelegbey, A. F., 2015. The econometrics of networks: A review. Working Paper  2015/13, Department of Economics, University of Venice.
• Clemens, M. A., 2015. The meaning of failed replications: A review and proposal. IZA Discussion Paper No.9000.
• Fair, R. C., 2015. Information limits of aggregate data. Discussion Paper No. 2011, Cowles Foundation, Yale University.
• Phillips, P. C. B., 2015. Inference in near singular regression. Discussion Paper No. 2009, Cowles Foundation, Yale University.
• Stock, J. H. and M. W. Watson, 2015. Core inflation and trend inflation. NBER Working Paper 21282.
• Ullah, A. and X. Zhang, 2015. Grouped model averaging for finite sample size. Working paper, Department of Economics, University of California, Riverside.

© 2015, David E. Giles

Questions About the Size and Power of a Test

Osman, a reader of this blog, sent a comment in relation to my recent post on the effects of temporal aggregation on t-tests, and the like. Rather than just bury it, with a short response, in the "Comments" section of that post, I thought I'd give it proper attention here.

The comment read as follows:

"Thank you for this illustrative example. My question is not exactly related to the subject of your post. As you illustrated, the finite sample properties of tests are studied by investigating the size and power properties. You reported size distortions to assess the size properties of the test. My first question is about the level of the size distortions. How much distortions is need to conclude that a test is useless? Is there an interval that we can construct around a nominal size value to gauge the significance of distortions? Same type of questions can also be relevant for the power properties. The “size adjusted power” is simply rejection rates obtained when the DGP satisfies an alternative hypothesis. Although, the power property is used to compare alternative tests, we can still ask question regarding to the level of the power. As your power curve shows, the level of power also depends on the parameter value assumed under the alternative hypothesis. For example, when β₁ = 0.8 the power is around 80% which means that the false null is rejected 80 times out of 100 times. Again, the question is that what should be the level of the power to conclude that the test has good finite sample properties?"

Let's look at Osman's questions.

'Student', on Kurtosis

W. S. Gosset (Student) provided this useful aid to help us remember the difference between platykurtic and leptokurtic distributions:

('Student', 1927. Errors of routine analysis. Biometrika, 19, 151-164. See p. 160.)

Here, β₂ is the fourth standardized moment of the distribution about its mean. The Normal distribution has β₂ = 3.

The appropriate definition of "kurtosis" for uni-modal distributions has been the subject of considerable discussion in the statistical literature. Should it be based on the characteristics of the tail of the distribution; the shape of the density around its mode; or both? 

© 2015, David E. Giles

Parallel Computing for Data Science

Hot off the press, Norman Matloff's book, Parallel Computing for Data Science: With Examples in R, C++ and CUDA  (Chapman and Hall/ CRC Press, 2015) should appeal to a lot of the readers of this blog.

The book's coverage is clear from the following chapter titles:

1. Introduction to Parallel Processing in R
2. Performance Issues: General
3. Principles of Parallel Loop Scheduling
4. The Message Passing Paradigm
5. The Shared Memory Paradigm
6. Parallelism through Accelerator Chips
7. An Inherently Statistical Approach to Parallelization: Subset Methods
8. Distributed Computation
9. Parallel Sorting, Filtering and Prefix Scan
10. Parallel Linear Algebra
Appendix - Review of Matrix Algebra 

The Preface makes it perfectly clear what this book is intended to be, and what it is not intended to be. Consider these passages:

July Reading

Now that the (Northern) summer is here, you should have plenty of time for reading. Here are some recommendations:

• Ahelegbey, D. F., 2015. The econometrics of networks: A review. Working Paper 13/WP/2015, Department of Economics, University of Venice.
• Camba-Mendez, G., G. Kapetanios, F. Papailias, and M. R. Weale, 2015. An automatic leading indicator, variable reduction and variable selection methods using small and large datasets: Forecasting the industrial production growth for Euro area economies. Working Paper No. 1773, European Central Bank.
• Cho, J. S., T-H. Kim, and Y. Shin, 2015. Quantile cointegration in the autoregressive distributed-lag modeling framework. Journal of Econometrics, 188, 281-300.
• De Luca, G., J. R. Magnus, and F. Peracchi, 2015. On the ambiguous consequences of omitting variables. EIEF Working Paper 05/15.
• Gozgor, G., 2015. Causal relation between economic growth and domestic credit in the economic globalization: Evidence from the Hatemi-J's test. Journal of International Trade and Economic Development,  24, 395-408.
• Panhans, M. T. and J. D. Singleton, 2015. The empirical economist's toolkit: From models to methods. Working Paper 2015-03, Center for the History of Political Economy.
• Sanderson. E and F. Windmeijer, 2015. A weak instrument F-test in linear IV models with multiple endogenous variables. Discussion Paper 15/644, Department of Economics, University of Bristol.

© 2015, David E. Giles