Alexander Aitken

Alexander Aitken was one of New Zealand's greatest mathematicians - see my earlier post. As an econometrician, you may be very surprised how much you owe him!

Want to check out more about this amazing man? See www.nzedge.com/alexander-aitken/ .

© 2014, David E. Giles

Testing Up, or Testing Down?

Students are told that if you're going to go in for sequential testing, when determining the specification of a model, then the sequence that you follow should be "from the general to the specific". That is, you should start off with a "large" model, and then simplify it - not vice versa.

At least, I hope this is what they're told!

But are they told why they should "test down", rather than "test up"? Judging by some of the things I read and hear, I think the answer to the last question is "no"!

The "general-to-specific" modelling strategy is usually attributed to David Hendry, and an accessible overview of the associated literature is provided by Campos et al. (2005).

Let's take a look at just one aspect of this important topic. 

Rob Hyndman on Forecasting

If you have an interest in forecasting, especially economic forecasting, the Rob Hyndman's name will be familiar to you. Hailing from my old stamping ground - Monash University - Rob is one of the world's top forecasting experts. 

Without going into all of the details, Rob is very widely published, and also has a great blog, Hyndsight. He's author of the well-known  "forecast" package for R (version 5 just released); and the co-author of several important books.

Last year, Rob taught an on-line forecasting course, titled, "Time Series Forecasting Using R". It comprised 12 one-hour lectures, on the following topics (with exercises):

• Introduction to forecasting 
• The forecaster's toolbox 
• Autocorrelation and seasonality 
• White noise and time series decomposition 
• Exponential smoothing methods 
• ETS models 
• Transformations and adjustments 
• Stationarity and differencing 
• Non-seasonal ARIMA models 
• Seasonal ARIMA models 
• Dynamic regression 
• Advanced methods

The really good news? You can access these presentations right here!

© 2014, David E. Giles

An ARDL Add-in for EViews

My posts on ARDL models and bounds testing (here and here) have certainly been popular. So, I was really pleased to see that Yashar Tarverdi has produced an "Add-In" for the EViews package that makes this type of econometric analysis somewhat easier.

You can download the the add-in program and its installer here. The add-in is called "ARDLbound", and it largely automates the key steps associated with bounds testing using an ARDL model.

Jim Hamilton on R-Squared and Economic Prediction

I always tell my students that, when it comes to regression results, the value of the coefficient of determination (R²), is pretty much the last thing that I look at. And I'm serious! I've blogged about this before (see here, for example), but it's worth reiterating, and I was reminded of this when I saw Jim Hamilton's post on this topic today.

Read it, and enjoy!

© 2014, David E. Giles

Six Word Peer Review

A "Six Word Peer Review" competition has been running on Twitter (#sixwordpeereview).

Here are a few gems that might be a little too close to home for comfort:

• You didn't cite my paper: Reject!
• Taking my time. Love, your competitor.
• Bayes would turn in his grave.
• Sorry for the huge delay. Reject!
• Author made all required revisions. Reject!
• Your conclusions contradict your actual results.
• Has author considered another direction entirely?

Not among the tweets, but the punch-line to a report I was handling as a member of the editorial board for Journal of Econometrics some years ago:

                        "This dog should be put down".

It's true - I swear!

© 2014, David E. Giles

Thanks a Milllion!

So,.......... by reading this post you'll assist in pushing the total number of page-views for this blog, since its inception in 2011, above the 1 Million mark. Thanks for your interest, support, and questions.

It's been a blast!

© 2014, David E. Giles

An Interesting New Book

Here's a new book that looks as if it will be interesting, and I'm looking forward to reading it myself: Panel Data Analysis Using Eviews, writen by I Gusti Ngurah Agung. Two other related books by this author have been published perviously - see here.

I'll give my opinion in more detail at a later date.

© 2014, David E. Giles

Estimating the Generalized Pareto Distribution

The generalized Pareto distribution (GPD) arises in the modelling of "extremes", especially if the "peaks-over-threshold" approach is being used. Estimating the parameters of the GPD by the method of maximum likelihood is especially challenging. The challenges arise because the likelihood function doesn't satisfy the usual regularity conditions for all possible values of the parameters.

I've discussed some of these issue in earlier posts, here and here.

When my colleagues, Helen Feng and Ryan Godwin, and I started looking at analytic bias reduction techniques for maximum likelihood estimators that can't be expressed in closed form, we first tackled the case of the GPD. It was well motivated, because you usually start with a very large sample size, the number of extreme data-points that lie above a given threshold, and which form the sample for estimation purposes, is generally very small. So, small-sample bias is a real issue.

Well, we bit off a lot more than we realized at the time, and bias reduction when estimating the GPD's parameters turned into a bit of a nightmare! We published several papers (including ones with Jacob Schwartz) dealing with bias reduction for other distributions, but the GPD problem was always there in the background.

I'm pleased to be able to report that our paper on this problem is now accepted for publication in Communications in Statistics - Theory & Methods. You can access a pre-print here.

© 2014, David E. Giles

Thanks for the Honour!

Recently, I became an Honorary Professor in the Department of Economics in the Management School at the University of Waikato, in New Zealand. The appointment took effect on 1 January.

The economics group at Waikato is very energetic and productive, so I'm really grateful for this honour and for the opportunity to interact with them .

I'm still on faculty, full-time, at the the University of Victoria in Canada. However, I'm expecting that my new link will enable me to play a more active role in the profession in New Zealand (where I studied and lived for many years) than has been possible in recent times.

So, a big "thank you" to those who made this wonderful opportunity possible!

© 2014, David E. Giles

Reading for the New Year

Back to work, and back to reading:

• Basturk, N., C. Cakmakli, S. P. Ceyhan, and H. K. van Dijk, 2013. Historical developments in Bayesian econometrics after Cowles Foundation monographs 10,14. Discussion Paper 13-191/III, Tinbergen Institute.
• Bedrick, E. J., 2013. Two useful reformulations of the hazard ratio. American Statistician, in press.
• Nawata, K. and M. McAleer, 2013. The maximum number of parameters for the Hausman test when the estimators are from different sets of equations.  Discussion Paper 13-197/III, Tinbergen Institute.
• Shahbaz, M, S. Nasreen, C. H. Ling, and R. Sbia, 2013. Causality between trade openness and energy consumption: What causes what  high, middle and low income countries. MPRA Paper No. 50832. 
• Tibshirani, R., 2011. Regression shrinkage and selection via the lasso: A retrospective. Journal of the Royal Statistical Society, B, 73, 273-282.
• Zamani, H. and N. Ismail, 2014. Functional form for the zero-inflated generalized Poisson regression model. Communications in Statistics - Theory and Methods, in press.

© 2014, David E. Giles

CESG 2014

The Canadian Econometrics Study Group has a long and illustrious history! This year, the 31st Meeting of the CESG is to be hosted by Simon Fraser University. The meeting will take place in early October 2014, and details can be found here.

This promises to be another great meeting, with invited addresses by Jushan Bai (Columbia University) and Guido Imbens (Stanford University).

© 2014, David E. Giles

The Three Minute Thesis

Now that all of you grad. students are excited that you'll be able to get your dissertations finished in just three minutes, let me bring you back to earth!

The Three Minute Thesis (3MT) is a competition that challenges research students to present the essence of their work, in just three minutes, to a lay audience. Communication is key. Originally developed by a group at the University of Queensland in Australia, the 3MT has now become popular in other parts of the world. including where in Canada. Campus, regional, and national competitions have emerged, with students from all disciplines vying for the honour of being a communication star.

It's a great concept, and it's good to see how many universities are coming on-board in supporting and promoting the 3MT. If you want to see what it's all about, there are some great video clips, here.

Here at the University of Victoria, our first 3MT competition is being organised by our Faculty of Graduate Studies, and the full details for local students are here. 

Cash prizes are at stake! You'll have to hurry, though, because entries are closing very soon.

© 2014, David E. Giles

Zero-One Matrices

When we're learning the basics of least squares regression analysis, one of the topics that we invariably encounter is the consequences of model mis-specification. In particular, we're taught that omitting relevant regress from the model renders the OLS estimator biased and inconsistent, although its precision is improved. On the other hand, including extraneous regressors simply reduces the efficiency of the OLS estimator of the coefficient vector. That estimator is still unbiased (and consistent) in this case.

These results are just special cases of those associated with imposing false restrictions on the parameter space, or failing to impose valid restrictions. So, once these more general results have been covered there's really no need to treat the "omitted regressors" and "extraneous regressors" situations as a separate matter.

However, usually they are dealt with as a distinct topic. What I find interesting, and what I want to focus on here, is the way in which the unbiasedness of OLS can be demonstrated in the context of irrelevant regressors. There's an easy way to get this result, and there's a more tedious proof. Let's begin by looking at the easy way.

Congratulations, Sir Richard!

Well-known microeconometrician, Richard Blundell, has been knighted in the New Year's Honours list. Sir Richard is the Ricardo Professor of Political Economy at University College London, and Research Director for the Institute for Fiscal Studies.

Knighted for his services to economics and social science, Sir Richard has previously served as co-editor of both Econometrica and Journal of Econometrics, and his many other achievement and awards are listed here.

I'm sure that all econometricians will be delighted by this recognition of Sir Richard's contributions, and offer him their sincere congratulations!

© 2014, David E. Giles