Recent Reading

From my "Recently Read" list:

• Born, B. and J. Breitung, 2014. Testing for serial correlation in fixed-effects panel data models. Econometric Reviews, in press.
• Enders, W. and Lee. J., 2011. A unit root test using a Fourier series to approximate smooth breaks, Oxford Bulletin of Economics and Statistics, 74, 574-599.
• Götz, T. B. and A. W. Hecq, 2014. Testing for Granger causality in large mixed-frequency VARs. RM/14/028, Maastricht University, SBE, Department of Quantitative Economics.
• Kass, R. E., 2011. Statistical inference: The big picture. Statistical Science, 26, 1-9.
• Qian, J. and L. Su, 2014. Structural change estimation in time series regressions with endogenous variables. Economics Letters, in press.
• Wickens, M., 2014. How did we get to where we are now? Reflections on 50 years of macroeconomic and financial econometrics. Discussion Paper No. 14/17, Department of Economics and Related Studies, University of York.

© 2014, David E. Giles

Testing......1, 2, 3, ......

I often think that most courses in econometric theory are somewhat unbalanced. Much more attention is given to estimation principles and estimator properties than is given to the principles of hypothesis testing, the properties of tests.

This always strikes me as somewhat ironic. In econometrics we're at least as interested in testing some interesting economic hypotheses as we are in estimating some particular parameters.

For that reason, even my introductory undergraduate "economic statistics" course always includes some basic material on the properties of tests. By this I mean properties such Uniformly Most Powerful; Locally Most Powerful; Consistent; and Unbiased. (With respect to the last two properties I do  mean test properties, not estimator properties.)

After all, when you're first learning about hypothesis testing, it's important to know that there are sound justifications for using the particular tests that are being taught. We don't use the "t-test" simply because it was first proposed by a brewer! Or, for that matter, because tables of critical values are in an appendix of our text book. We use it because, under certain circumstances, it is Uniformly Most Powerful (against one-sided alternative hypotheses).

If tests aren't motivated and justified in this sort of way, we're just dishing out recipes to our students. And I've never liked the cookbook approach to the teaching of statistics or econometrics.

There's a lot to blog about when it comes to hypothesis testing. In some upcoming posts I'll try and cover some testing topics which, in my view, are given too little attention in traditional econometrics courses.

To whet your appetite - the first two will be about the distributions of some standard test statistics when the null hypothesis is false; and how this information can be used to compute some power curves.

© 2014, David E. Giles

Econometrics Term Test

A few days ago the students in my introductory graduate Econometrics course had their mid-term test.

Here's the test, and a brief solution.

How did you fare?

© 2014, David E. Giles

Would You Like Some Hot Potatoes?

O.K., I know - that was a really cheap way of getting your attention.

However, it worked, and this post really is about Hot Potatoes - not the edible variety, but some teaching apps. from "Half-Baked Software" here at the University of Victoria.

To quote: 

"The Hot Potatoes suite includes six applications, enabling you to create interactive multiple-choice, short-answer, jumbled-sentence, crossword, matching/ordering and gap-fill exercises for the World Wide Web. Hot Potatoes is freeware, and you may use it for any purpose or project you like."

I've included some Hot Potatoes multiple choice exercises on the web pages for several of my courses for some years now. Recently, some of the students in my introductory graduate econometrics course mentioned that these exercises were quite helpful. So, I thought I'd share the Hot Potatoes apps. for that course with readers of this blog.

There are eight multiple-choice exercise sets in total, and you can run  them from here:

Quiz 1 ; Quiz 2 ; Quiz 3 ; Quiz 4; Quiz 5 ; Quiz 6 ; Quiz  7 ; Quiz 8 .

I've also put the HTML and associated PDF files on the code page for this blog. If you're going to download them and use them on your own computer or website, just make sure that the PDF files are located in the same folder (directory) as the HTML files. I plan to extend and update these Hot Potatoes exercises in the near future, but hopefully some readers will find them useful in the meantime. © 2014, David E. Giles

Econometric Research Resources

The following page, put together by John Kane at the Department of Economics, SUNY-Oswego, has some very useful links for econometrics students and researchers: Econometric Research Resources. 

© 2014, David E. Giles

Illustrating Asymptotic Behaviour - Part III

This is the third in a sequence of posts about some basic concepts relating to large-sample asymptotics and the linear regression model. The first two posts (here and here) dealt with items 1 and 2 in the following list, and you'll find it helpful to read them before proceeding with this post:

1. The consistency of the OLS estimator in a situation where it's known to be biased in small samples.
2. The correct way to think about the asymptotic distribution of the OLS estimator.
3. A comparison of the OLS estimator and another estimator, in terms of asymptotic efficiency.

Here, we're going to deal with item 3, again via a small Monte Carlo experiment, using EViews.

Nobel Prize, 2014

From the website of the Royal Swedish Academy of Sciences:

The Prize in Economic Sciences 2014

The Royal Swedish Academy of Sciences has decided to award the Sveriges Riksbanks Prize in Economic Sciences in Memory of Alfred Nobel for 2014 to Jean Tirole, Toulouse 1 Capitole University, France

“for his analysis of market power and regulation”.

Mark Thoma has an excellent round-up of related links on his blog, Economist's View.


© 2014, David E. Giles

Illustrating Asymptotic Behaviour - Part II

This is the second in a sequence of three posts that deal with large-sample asymptotics - especially in the context of the linear regression model. The first post dealt with item 1 in this list:

1. The consistency of the OLS estimator in a situation where it's known to be biased in small samples.
2. The correct way to think about the asymptotic distribution of the OLS estimator.
3. A comparison of the OLS estimator and another estimator, in terms of asymptotic efficiency.

No surprise, but this post deals with item 2. To get the most out of it, I strongly recommend reading the first post before proceeding.

Illustrating Asymptotic Behaviour - Part I

Learning the basics about the (large sample) asymptotic behaviour of estimators and test statistics is always a challenge. Teaching this material can be challenging too!

So, in this post and in two more to follow, I'm going to talk about a small Monte Carlo experiment that illustrates some aspects of the asymptotic behaviour of the OLS estimator. I'll focus on three things:

1. The consistency of the OLS estimator in a situation where it's known to be biased in small samples.
2. The correct way to think about the asymptotic distribution of the OLS estimator.
3. A comparison of the OLS estimator and another estimator, in terms of asymptotic efficiency.

October Reading

October already!

• Chauvel, C. and J. O'Quigley, 2014. Tests for comparing estimated survival functions. Biometrika, 101, 535-552. 
• Choi, I., 2014. Unit root tests for dependent and heterogeneous micropanels. Discussion Paper No. 2014-04, Research Institute for Market Economy, Sogang University.
• Cho, J. S. and H. White, 2014. Testing the equality of two positive-definite matrices with application to in formation matrix testing. Discussion Paper, School of Economics,Yonsei University.
• Hansen, B. E., 2013. Model averaging, asymptotic risk, and regressor groups. Quantitative Economics, in press.
• Miller, J. I., 2014. Simple robust tests for the specification of high-frequency predictors of a low-frequency series. Mimeo., Department of Economics, University of Missouri.
• Owen, A. B. and P. A. Roediger, 2014. The sign of the logistic regression coefficient. American Statistician, in press.
• Westfall, P. H., 2014. Kurtosis as peakedness, 1905-2014. R.I.P.. American Statistician, 68, 191-195.

© 2014, David E. Giles