Free Download of My Recent Paper

If you're interested, you can download a copy of one of my recent papers, with Helen Feng and Ryan Godwin, for free from the publisher's website.

The paper in question is titled, "On the Bias of the Maximum Likelihood Estimator for the Two-Parameter Lomax Distribution", Communications in Statistics - Theory and Methods, 2013, 42, 1934-1950.

Only the first 50 downloads are free, so if you're interested you'd better get cracking!

© 2013, David E. Giles

Can You Actually TEST for Multicollinearity?

When you're undertaking a piece of applied econometrics, something that's always on your mind is the need to test the specification of your model, and to test the validity of the various underlying assumptions that you're making. At least - I hope it's always on your mind!

This is an important aspect of any modelling exercise, whether you're working with a linear regression model, or with some nonlinear model such Logit, Probit, Poisson regression, etc. Most people are pretty good when it comes to such testing in the context of the linear regression model. They seem to be more lax once they move away from that framework. That makes me grumpy, but that's not what this particular post is about.

It's actually about a rather silly question that you sometimes encounter, namely: "Have you tested to see if multicollinearity is a problem for your results?"

I'll explain why this isn't really a sensible question, and why the answer to the question in the title for this post is a resounding "No!"

ARDL Models - Part II - Bounds Tests

[Note: For an important update of this post, relating to EViews 9, see my 2015 post, here.]

Well, I finally got it done! Some of these posts take more time to prepare than you might think.

The first part of this discussion was covered in a (sort of!) recent post, in which I gave a brief description of Autoregressive Distributed Lag (ARDL) models, together with some historical perspective. Now it's time for us to get down to business and see how these models have come to play a very important role recently in the modelling of non-stationary time-series data.

In particular, we'll see how they're used to implement the so-called "Bounds Tests", to see if long-run relationships are present when we have a group of time-series, some of which may be stationary, while others are not. A detailed worked example, using EViews, is included.

Vintage Years in Econometrics: The 1940's

A Fathers' Day "thank you" to our founding fathers..........

Following on from my earlier post about vintage years for econometrics in the 1930's, here's my take on the 1940's. This is a more challenging decade to assess, given the explosion of major contributions in the second decade of life for the new discipline.

As before, let me note that "in econometrics, what constitutes quality and importance is partly a matter of taste - just like wine! So, not all of you will agree with the choices I've made in the following compilation."

I've added a few "tasting notes" here and there, if I thought they were warranted.

When is an Autoregressive Model Dynamically Stable?

Autoregressive processes arise frequently in econometrics. For example, we might have a simple dynamic model of the form:

            y_t = β₀ + β₁y_t-1 + ε_t   ;   ε_t ~ i.i.d.[0 , σ²]       .           (1)

Or, we might have a regression model in which everything is "standard", except that the errors follow an autoregressive process:

            y_t = β₀ + β₁x_t + u_t               (2)

             u_t = ρ u_t-1 + ε_t    ;  ε_t ~ i.i.d.[0 , σ²] .

In each of these examples a first-order autoregressive, or AR(1), process is involved.

Higher-order AR processes are also commonly used. Although most undergrad. econometrics students are familiar with the notion of "stationarity" in the context of an AR(1) process, often they're not aware of the conditions needed to ensure the stationarity of more general AR models. Let's take a look at this issue.

Special Issues of "Computational Statistics & Data Analysis"

One of the statistics journals that's always on my watch-list (and for whom I sometimes referee) is Computational Statistics and data Analysis. CSDA regularly publishes issues devoted to special topics, including topics explicitly related to econometrics. Indeed, six special issues on Computational Econometrics have been published to date, including the 1^st issue of the Annals of Computational and Financial Econometrics, released last year.

Recently, the call went out for submissions for two further special issues:

• Time Series Econometrics
• Bayesian Econometrics

Judging by the quality of past special issues, these two will be ones to watch out for!

© 2013, David E. Giles

Advice for Graduate Students

This is post is for you grad. students in econometrics. In most parts of the world you'll be working in an economics department, so the links that follow should be pretty relevant: 

• John Cawley, A Guide and Advice for Economists on the U.S. Junior Academic Job Market
• Kwan Choi, Advice for Acadmic Authors
• John Cochrane, Writing Tips for Ph.D. Students
• Don Davis, Ph.D. Thesis Research: Where do I Start?
• Daniel Hamermesh, The Young Economist's Guide to Professional Etiquette
• Darren Lubotsky, A Few Tips for Being a More Sucessful Graduate Student
• Matthew Pearson, How to Survive Your First Year of Graduate School in Economics
• Ariel Rubinstein, 10 Q&A: Experienced Advice for "Lost" Graduate Students in Economics
• Jonathon Shewchuk, Giving an Academic Talk

I can also recommend the blog, The Professor Is In. Authored by anthropologist Karen Kelsky, it offers much sage advice to grad. students of all stripes. Including econometricians!

© 2013, David E. Giles

What Have You Been Reading?

Here are some of the papers that I was reading last week:

• Arel-Bundock, V., 2013. A solution to the weak instrument bias in 2SLS estimation: Indirect inference with stochastic approximation, Economics Letters, in press.
• Behar, R., P. Grima, and L. Marco-Almagro, 2013. Twenty-five analogies for explaining statistical concepts. American Statistician, 67(1), 44-48.
• Chang, C-L., P. H. Frances, and M. McAleer, 2013, Are forecast updates progressive? MPRA Paper No. 46387.
• Chortareas, G., and G. Kapetanios, 2013. How puzzling is the PPP puzzle? An alternative half-life measure of convergence to PPP. Journal of Applied Econometrics, 28, 435-457.
• Davidson, R. and J. B. MacKinnon, 1998. Graphical methods for investigating the size and power of hypothesis tests. Manchester School, 66, 1-26.
• Hood, W. C. and T. C. Koopmans, 1953. Studies in Econometric Method. Cowles Commission Monograph for Research in Economics, Monograph No. 14. Wiley, New York.
• Kourouklis, S., 2012. A new estimator of the variance based on minimizing mean squared error. American Statistician, 66(4), 234-236.
• Lanne, M. and P. Saikkonen, 2013. Noncausal vector autoregression. Econometric Theory, 29, 447-482.

© 2013, David E. Giles

The Ultimate Probability Distribution Explorer

To give it its full name, The Ultimate Univariate Probability Distribution Explorer is one of the (**free**) tools released recently by Wolfram  Research for use with their (**free**) CDF viewer.

First, what's CDF? This stands for "Computable Document Format", and I suggest you read my earlier posts on it, here and here. You can download the player using this link.

A post on the Wolfram blog alerted me to the availability of The Ultimate Univariate Probability Distribution Explorer, which is a CDF utility that you can download from here.

You can use this utility to explore the various properties of 500 univariate probability distributions.

Simulating Critical Values for Some Test Statistics

This post comes at the request of Francesca, in a comment on an earlier post on Monte Carlo simulation.

The request was for some examples of how we can compute finite-sample critical values for different test statistics.  What this really means is that we want to simulate particular "quantiles" (points on the X-axis) for the distribution of the statistic.

Let's see what we can do about this. First, some background information.

Last Week's Reading

There are some great econometrics papers out there, just waiting to be read. I need more hours in the day!

Some of the papers I enjoyed reading last week were:

• Barsoum, F. and S. Stankiewicz, 2013. Forecasting GDP growth using mixed-frequency models with switching regimes. Department of Economics, University of Konstanz, Working Paper 2013-10.
• Castle, J. L., M. P. Clements, and D. F. Hendry, 2013. Forecasting by factors, by variables, by both or neither? Journal of Econometrics, in press.
• Chiu, C. W., B. Eraker, A. T. Foerster, T. B. Kim, and H. D. Seoane, 2012. Estimating VAR's sampled at mixed or irregular spaced frequencies: A Bayesian approach. Federal Reserve Bank of Kansas City, Research Working Paper 11-11 (revised, December 2012).
• Dufour, J-M. and J. Wilde, 2013. Weak identification in probit models with endogenous covariates.
• Lim, H. K., J. Song, and B. C. Jung, 2013. Score tests for zero-inflation and overdispersion in two-level count data. Computational Statistics and Data Analysis, 61, 67-82.
• Millimet, D. L. and I. K. McDonough, 2013. Dynamic panel data models with irregular spacing: With applications to early childhood development. IZA Discussion Paper 7359.
• Pesaran, H. H., A. Pick, and M. Pranovich, 2013. Optimal forecasts in the presence of structural breaks. Journal of Econometrics, in press.

© 2013, David E. Giles

Vintage Years in Econometrics - The 1930's

We all know that when it comes to wine-making, some years yield better wine than others. If you like to sip a little wine while looking at pictures, then The Wine Advocate's "Vintage Chart" may appeal to you. (It's just a pity that they don't acknowledge the fact that there's more than one wine-producing region in New Zealand!)

That got me thinking about vintage years for econometrics. Funny how the mind works, sometimes, isn't it?

So, this post is for you budding students of econometrics. Our future lies with you, but it's not a bad thing to know something about our past!