Tuesday, August 4, 2015

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

Thursday, July 16, 2015

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 β= 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.

Thursday, July 9, 2015

'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

Wednesday, July 8, 2015

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:

Tuesday, June 30, 2015

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

Monday, June 29, 2015

The Econometrics of Temporal Aggregation - VI - Tests of Linear Restrictions

This post is one of several related posts. The previous ones can be found here, here, here, here and here. These posts are based on Giles (2014).

Many of the statistical tests that we perform routinely in econometrics can be affected by the level of aggregation of the data. Here, Let's focus on time-series data, and with temporal aggregation. I'm going to show you some preliminary results from work that I have in progress with Ryan Godwin. These results relate to one particular test, but work covers a variety of testing problems.

I'm not supplying the EViews program code that was used to obtain the results below - at least, not for the moment. That's because the results that I'm reporting are based on work in progress. Sorry!

As in the earlier posts, let's suppose that the aggregation is over "m" high-frequency periods. A lower case symbol will represent a high-frequency observation on a variable of interest; and an upper-case symbol will denote the aggregated series.

               Yt = yt + yt - 1 + ......+ yt - m + 1 .

If we're aggregating monthly (flow) data to quarterly data, then m = 3. In the case of aggregation from quarterly to annual data, m = 4, etc.

Now, let's investigate how such aggregation affects the performance of standard tests of linear restrictions on the coefficients of an OLS regression model. The simplest example would be a t-test of the hypothesis that one of the coefficients is zero. Another example would be the F-test of the hypothesis that all of the "slope" coefficients in such a regression model are zero.

Consider the following simple Monte Carlo experiment, based on 20,000 replications.

Monday, June 22, 2015

"Readers' Forum" Page

I've added a new page to this blog - it's titled "Readers' Forum". You'll see the tab for it in the bar just above the top post on the page you're reading now.

Some explanation is in order.

The Readers' Forum is intended to be a "clearing house" for questions and requests relating to econometrics'

As I say in the preamble to the new page,
"Please feel free to use the "Comment" facility below to provide questions and answers relating to Econometrics. I won't be able to answer all questions myself, but other readers may be able to help. The Forum will be "lightly moderated" to avoid spam and inappropriate content."
Please note the italicized passage above.

Every day, readers post (or attempt to post) lots of "comments" on the various posts on this blog. In many cases, these are not comments at all - they're questions, requests for assistance, and the like.

All comments are moderated - I get to give them the "O.K." before they actually appear. That's just fine for genuine comments. Unless they're spam or contain inappropriate content, I invariably "approve" them right away.

However, in the case of questions and requests I prefer to delay approval and post a response simultaneously. Regrettably, this has meant that, increasingly, there is often a delay in getting this out there. 

Sometimes, the requests are, quite frankly, unreasonable.I won't go into the details here, but let's just say that there's a difference between a blog and a free consulting service.

I also try to be very careful when it's clear that the request comes form a student. I certainly don't want to get into a situation where I'm "getting between" that student and their instructor/supervisor.

In short, I like to try and be helpful, but I can't keep up with the demand. I do have a job!

Hopefully, the new forum will free up some time for me to focus on substantive posts, while still providing an opportunity for discussion.

© 2015, David E. Giles

Friday, June 12, 2015

Econometrics Videos

The Royal Economics Society (publisher of The Econometrics Journal) has recently released a video of invited addresses by Alfred Galichon and Jeremy Lise, in the special session on “Econometrics of Matching” at the 2015 RES Conference.

This video joins similar ones from previous RES conferences, these being:

  • “Large Dimensional Models”, 
  • ”Heterogeneity”,  
  • “Econometrics of Forecasting”, 
  •  “Nonparametric Identification” 
This link will take you to all of these videos.

Happy viewing!

© 2015, David E. Giles

Specification Testing in the Ordered Probit Model

Readers of this blog will know I'm a proponent of more specification testing in the context of Logit, Probit, and related models. For, instance, see this recent post, and the links within it.

I received an email from Paul Johnson, Chair of the Department of Economics at Vassar College, today. He wrote:
"I thought that, given your interest in specification tests in probit etc. models, you might find the attached paper of mine (written some years ago) to be useful as it expounds the straightforward generalization of the Bera, et al. (1984) test to the ordered probit case."
Paul's paper is, indeed, very interesting and I hadn't seen it before. It's titled, "A Test of the Normality Assumption in the Ordered Probit Model", and it appeared in the statistics journal, Metron (1996, LIV, 213-221).

Here's the abstract:
"This paper presents an easily implemented test of the assumption of a normally distributed error term for the ordered probit model, As this assumption is the central maintained hypothesis in all estimation and testing based on this model, the test ought to serve as a key specification test in applied research. A small Monte Carlo experiment suggests that the test has good size and power properties."
A year later, a closely related paper by P. Glewwe appeared in Econometric Reviews. That author doesn't mention Paul's paper, but these things happen. Glewwe's paper does take things a little further than Paul does, by allowing for censoring of the data.

© 2015, David E. Giles

Tuesday, June 9, 2015

Worrying About my Cholesterol Level

The headline, "Don't Get Wrong Idea About Cholesterol", caught my attention in the 3 May, 2015 Times-Colonist newspaper here in Victoria, B.C.. In fact the article came from a syndicated column, published about a week earlier. No matter - it's always a good time for me to worry about my cholesterol!

The piece was written by a certain Dr. Gifford-Jones (AKA Dr. Ken Walker).

Here's part of what he had to say: