April Reading List

Here are some of the paper that I've been reading lately:

• Armstrong, J. S., K. C. Green, and A. Graefe, 2014. Golden rule of forecasting: Be conservative. MPRA Paper No. 53579
• Ashley, R. A. and K. P. Tsang, 2014. Credible Granger-causality inference with modest sample lengths: A cross-sample validation approach. Econometrics, 2, 72-91.
• Imbens, G. W., 2014. Instrumental variables: An econometrician's perspective. IZA DP No. 8048, Institute for the Study of Labor, Bonn.
• Kleijnen, J. P. C., 2014. Response surface methodology. Discussion Paper 2014- 013, CentER, Tilburg University.
• Pesaran, M. H. and R. P. Smith, 2014. Signs of impact effects in time series regression models. Economics Letters, 122, 150-153.

© 2014, David E. Giles

Understanding the Underlying Asumptions

From time to time I've been known to blog about the importance of fully understanding the assumptions that underlie the various estimators and tests that we use in econometrics. (Here, too.) Gee, I've even gone so far as to suggest that students should learn about these assumptions by taking courses where results are proved formally- not introduced simply through arm-waving.

I'm not going to start griping about all of that again here - it's too nice a Spring day for that.

However, I've just been reading a recent piece in Scientific American that's relevant to my main concern when students are taught "how to do" econometrics, but don't have a proper understanding of the underlying assumptions. That concern is simply that, sooner or later, they'll screw up!

Maybe it won't be the end of the world. The economy probably won't collapse in a big messy heap. Perhaps they'll just lose their job!

The S.A. article was about just this sort of thing - but in the case of neuroscientists, not economists. For the sake of full disclosure I have nothing at all against neuroscientists. In fact, I have a daughter who is doing post-grad. work in just that field at the Florey Institute in Australia.

You can read the article for yourself, and I hope that you will. In a nutshell, there have been numerous influential neuroscience studies, that have appeared in the very top scientific journals, and which have been based on fundamentally flawed statistical analysis. 

To put it really simply, the authors have used statistical tests whose validity require that the data have been sampled independently, when in fact this requirement is undeniably violated in these studies.

Oh dear!

To quote Gary Stix, the author of the article:

'Emery N. Brown, a professor of computational neuroscience in the department of brain and cognitive sciences at the MIT-Harvard Division of Health Sciences and Technology, points to a dire need to bolster the level of statistical sophistication brought to bear in neuroscience studies. “There’s a fundamental flaw in the system and the fundamental flaw is basically that neuroscientists don’t know enough statistics to do the right things and there’s not enough statisticians working in neuroscience to help that." ' 

I'd venture to guess that "screwing up" in the neurosciences might have some unpleasant consequences.

Needless to say, I've sent the link to the S.A. article to my daughter!

You might want to think about this the next time you fire up your favourite econometrics package: Did your friendly econometrics instructor make sure that you really understand the assumptions that need to be satisfied before you can rely on the estimators and tests you're about to use?

© 2014, David E. Giles

MCMC for Econometrics Students - IV

This is the fourth in a sequence of posts designed to introduce econometrics students to the use of Markov Chain Monte Carlo (MCMC, or MC²) simulation methods for Bayesian inference. The first three posts can be found here, here, and here, and I'll assume that you've read them already. The emphasis throughout is on the use of the Gibbs sampler.

The first three posts took a look "inside the box", to see that the Gibbs sampler entails. I looked at some R code that could be used to show the sampler "in action". One way to think about those posts is that they were analogous to an explanation of OLS regression, with code that assembled the X matrix, the (X'X) matrix, showed you how to invert the latter matrix, and so on. It's important to understand what is going on when you select "OLS" in your favourite package, but you certainly wouldn't dream of constructing the estimator from first principles every time you wanted to use it.

It's the same in the case of our Bayesian analysis. So, the purpose of this post is to show you simple it is, in practice, to use R to estimate regression models using Bayesian methods, and to implement Bayesian Posterior Odds analysis for model selection. We can just take advantage of the R packages that have been developed already to help us.

Congratulations to My Colleague!

This afternoon I had the pleasure of attending the annual Teaching, Research, and Service Awards ceremony for our Faculty of Social Sciences here at the University of Victoria. Our Dean, Peter Keller, does a great job with this event. I was especially grateful for the food, as I'd missed out on lunch today!

However, the real highlight was the presentation of the Research Award to my colleague, Kees van Kooten. Kees is an outstanding researcher who holds a (Tier 1) Canada Research Chair in our department, and the award was both richly deserved and long overdue.

Congratulations, Kees!

© 2014, David E. Giles

Thumbs Up; Thumbs Down

People say and do the darnedest things! 

I'll let you assign your own "thumbs up" and "thumbs down" to the following gems. I imagine you can guess where I stand on each of them!

'But which is a bigger menace to society, laziness about data or laziness about theory? Theory-laziness is seductive because it's easy - mining for correlations isn't very mentally taxing. But data-laziness is seductive because it's hard - the more complicated and intricate a theory you make, the smarter it makes you feel, even if the theory sucks. 

 In the past, data-laziness was probably more of a threat to humanity. Since systematic data was scarce, people had a tendency to sit around and daydream about how stuff might work. But now that Big Data is getting bigger and computing power is cheap, theory-laziness seems to be becoming more of a menace. The lure of Big Data is that we can get all our ideas from mining for patterns, but A) we get a lot of false patterns that way, and B) the patterns insidiously and subtly suggest interpretations for themselves, and those interpretations are often wrong.'

(Noah Smith in his post, Which is Better, Data or Theory?)

'........ which raises the question "who should be teaching students econometrics?" Should it be someone like ****, who is basically an applied micro guy, or should it be an econometric theorist?' 

(Frances Woolley, commenting on her own post)

'Developing statistical methods is hard and often frustrating work. One of the under appreciated rules in statistical methods development is what I call the 80/20 rule (maybe could even by the 90/10 rule). The basic idea is that the first reasonable thing you can do to a set of data often is 80% of the way to the optimal solution. Everything after that is working on getting the last 20%.'

(Jeff Leek, on the Simply Statistics blog)

'The micro stuff that people like myself and most of us do has contributed tremendously and continues to contribute. Our thoughts have had enormous influence. It just happens that macroeconomics, firstly, has been done terribly and, secondly, in terms of academic macroeconomics, these guys are absolutely useless, most of them. Ask your brother-in-law. I’m sure he thinks, as do 90% of us, that most of what the macro guys do in academia is just worthless rubbish. Worthless, useless, uninteresting rubbish, catering to a very few people in their own little cliques.'

(Chris Auld, reputedly quoting someone else, in a blog post from 2011)

'The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.'

(John Tukey)

'So, we produce our papers, as if on a relentless production line. We cannot wait for inspiration; we must maintain our output. To do our jobs successfully, we need to acquire a fundamental academic skill that the scholars of old generally did not possess; modern academics must be able to keep writing and publishing even when they have nothing to say. ....'

(Michael Billig, as quoted by Timothy Taylor)

So, thumbs up, and thumbs down. Or, from the sublime to the ridiculous - take your pick. Boy - it was hard to resist giving my reaction  to some of these! © 2014, David E. Giles

Data Transfer Advice From Francis Smart

I always enjoy reading the posts by Francis Smart on his Econometrics by Simulation blog. A couple of days ago he wrote a nice piece titled, "It is Time for RData Files to Become the Standard for Data Transfer". 

Francis made some very good points about the handling of large amounts of data, and he provided some convincing examples regarding the compression rates and opening times for RData files as compared with other options. The comments to his post are also very relevant.

If you're using or exchanging large data files, you'll find Francis's post most helpful.

© 2014, David E. Giles

Death of A. L. Nagar

I was saddened to learn that Anirudh Lal Nagar passed away on 4 February 2014. 

Nagar was an exceptional  Indian statistician and econometrician who made many fundamental contributions to our discipline. He was 83 years old at the time of his death.

Nagar (left) at the inauguration of the laboratory named in his honour at Jawaharlal Nehru University.

Nagar's work on the finite-sample inference in econometrics is especially well known. The term "the Nagar expansion" was coined by Denis Sargan (1974). This technique, proposed by Nagar in 1959 for the k-class estimators, was widely used to determine the sampling properties (bias, variance, etc.) of various simultaneous equations estimators. It came at a time when large-n asymptotics dominated the scene, and finite-sample results were deemed to be "intractable".

Nagar's work influenced a generation of theoretical econometricians, and paved the way for some of the most important results established in our discipline. The impact of his work is reflected in the volume of papers assembled to honour him on is sixtieth birthday (Carter et al., 1990).

He will be greatly missed.

References

Carter, R. A. L., J. Dutta, and A. Ullah (eds.), 1990. Contributions to Econometric Theory and Applications: Essays in Honour of A. L. Nagar. Wiley, New York. (Softcover reprint.)

Nagar, A. L., 1959. The bias and moment matrix of the general k-class estimators of the parameters in simultaneous equations. Econometrica, 27, 573-595. 

Sargan, J. D., 1974. The validity of Nagar's expansion for the moments of econometric estimators. Econometrica, 42, 169-176.

© 2014, David E. Giles

MCMC for Econometrics Students - III

As its title suggests, this post is the third in a sequence of posts designed to introduce econometrics students to the use of Markov Chain Monte Carlo (MCMC, or MC²) methods for Bayesian inference. The first two posts can be found here and here, and I'll assume that you've read both of them already.

We're going to look at another example involving the use of the Gibbs sampler. Specifically, we're going to use it  to extract the marginal posterior distributions from the joint posterior distribution, in a simple two-parameter problem. The problem - which we'll come to shortly - is one in which we actually know the answer in advance. That's to say, the marginalizing can be done analytically with some not-too-difficult integration. This means that we have a "bench mark" against which to judge the results generated by the Gibbs sampler.

Let's look at the inference problem we're going to solve.

MCMC for Econometrics Students - II

This is the second in a set of posts about Monte Carlo Markov Chain (MCMC, or MC²) methods in Bayesian econometrics. The background was provided in this first post, where the Gibbs sampler was introduced.

The main objective of the present post is to convince you that this MCMC stuff actually works!

To achieve this, what we're going to do is work through a simple example - one for which we actually know the answer in advance. That way, we'll be able to check our results from applying the Gibbs sampler with the facts. Hopefully, we'll then be able to see that this technique works - at least for this example! 

I'll be using some R script that I've written to take students through this, and it's available on the code page for this blog. I should  mention in advance that this code is not especially elegant. It's been written, quite deliberately, in a step-by-step manner to make it relatively transparent to non-users of R. Hopefully, the comments that are embedded in the code will also help.

It's also important to note that this first illustration of the Gibbs sampler in action does not involve the posterior distribution for the parameters in a Bayesian analysis of some model.  Instead, we're going to look at the problem of obtaining the marginals of a bivariate normal distribution, when we know the form of the conditional distributions.

In other words - let's proceed one step at a time. The subsequent posts on this topic will be dealing with Bayesian posterior analysis.

Let's take a look at the set-up, and the analysis that we're going to undertake.

MCMC for Econometrics Students - I

This is the first of a short sequence of posts that discuss some material that I use when teaching Bayesian methods in my graduate econometrics courses.

This material focuses on Markov Chain Monte Carlo (MCMC) methods - especially the use of the Gibbs sampler to obtain marginal posterior densities. This first post discusses some of the computational issues associated with Bayesian econometrics, and introduces the Gibbs sampler. The follow-up posts will illustrate this technique with some specific examples.

So, what's the computational issue here?