Summer Reading

The schools are out, and here in Canada we celebrated Canada Day yesterday. That means it's now summer! And summer means summer reading.

So, here are some suggestions for you:

• Andreou, E., E. Ghysels, and A. Kourtellos, 2013. Should macroeconomic forecasters use daily financial data and how? Journal of Business and Economic Statistics, 31, 240-251.
• Downey, A. B., 2013. Think Bayes: Bayesian Statistics Made Simple. Green Tea Press, Needham MA.
• Espejo, M. R., M. D. Pineda, and S. Nadarajah, 2013. Optimal unbiased estimation of some population central moments. Metron, 71, 39-62.
• Giacomini, R., D. M. Politis, and H. White, 2013. A warp-speed method for conducting Monte Carlo experiments involving bootstrap estimators. Econometric Theory, 29, 567-589.
• Hayter, A. J., 2013. A new procedure for the Behrens-Fisher problem that guarantees confidence levels. Journal of Statistical Theory and Practice, 7, 515-536.
• Ouysse, R., 2013. Forecasting using a large number of predictors: Bayesian model averaging versus principal components regression. Australian School of Business Working Paper 2013 ECON 04, University of New South Wales.
• Pinkse, J., 2013. The ET interview: Herman Bierens. Econometric Theory, 29, 590-608.
• Stigler, S. M., 2007.  The epic story of maximum likelihood. Statistical Science, 22, 598-620.
• Yu, P., 2013. Inconsistency of 2SLS estimators in threshold regression with endogeneity. Economics Letters, in press.

© 2013, David E. Giles

N.Z. Association of Economists Conference

Although it's still the afternoon of Tuesday 2 July here on the We(s)t Coast, it's already the morning of Wednesday 3 July in New Zealand. That being the case, the 54th Annual Conference of the New Zealand Association of Economists is just getting underway in Wellington. Although I'm not attending, I do have a soft-spot for this conference, and I'll be participating next year.

The conference program includes a number of interesting looking empirical papers, and as usual there is a strong emphasis on economic policy analysis.

The other reason for my interest in this conference? The first conference paper I ever presented was at the 1972 NZAE Conference, held at Massey University in Palmerston North. I talked about "Consumption Expenditure in New Zealand". How time flies!

© 2013, David E. Giles

Congratulations, Graham Voss!

Congratulations to my departmental colleague, Graham Voss, whose promotion to full Professor takes effect today!

Graham describes his research interests as: "Applied macroeconomics with a focus on monetary and fiscal policies and exchange rates". He's a very accomplished empirical macroeconomist, with extensive experience at The Reserve Bank of Australia (their central bank) to complement his academic contributions.

Here's his webpage.

© 2013, David E. Giles

The Bootstrap - A Non-Technical Introduction

Computer-intensive methods have become essential to much of statistical analysis, and that includes econometrics. Think of Monte Carlo simulations, MCMC for Bayesian methods, maximum simulated likelihood, empirical likelihood methods, the jackknife, and (of course) the bootstrap.

Although we usually date the bootstrap from Bradley Efron's 1979 paper, as a resampling method it has its roots in earlier, related, contributions including those of Quenouille (1949, 1956).

The main purpose of this post is to draw readers' attention to the piece by Diaconis and Efron (1983) that appeared in Scientific American. It's written for a "general audience", which is nice, and it also provides an interesting snapshot of what was cutting-edge computing 30 years ago. The discussion paper version of the article (including typos) is available here.

As a final bonus, the examples include one from econometrics!


References

Diaconis and B. Efron, 1983. Computer intensive methods in statistics. Scientific American, 248, 116-132.

Efron, B., 1979. Bootstrap methods: Another look at the jackknife. Annals of Statistics, 7, 1-26.

Quenouille, M. H.,1949. Approximate tests of correlation in time series. Journal of the Royal

Statistical Society, Series B, 11, 18-44.

Quenouille, M. H.,1956. Notes on bias in estimation. Biometrika, 61, 353-360.

© 2013, David E. Giles

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