November Reading

You'll see that this month's reading list relates, in part, to my two recent posts about Ted Anderson and David Cox.

• Acharya, A., M. Blackwell, & M. Sen, 2015. Explaining causal findings without bias: Detecting and assessing direct effects. RWP15-194, Harvard Kennedy School.
• Anderson, T.W., 2005. Origins of the limited information maximum likelihood and two-stage least squares estimators. Journal of Econometrics, 127, 1-16.
• Anderson, T.W. & H. Rubin, 1949. Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 20, 46-63.
• Cox, D.R., 1972. Regression models and life-tables (with discussion). Journal of the Royal Statistical Society B, 34, 187–220.
• Malsiner-Walli, G. & H. Wagner, 2011. Comparing spike and slab priors for Bayesian variable selection. Austrian Journal of Statistics, 40, 241-264.
• Psaradakis, Z. & M. Vavra, 2016. Portmanteau tests for linearity of stationary time series. Working Paper 1/2016, National Bank of Slovakia.

© 2016, David E. Giles

T. W. Anderson: 1918-2016

Unfortunately, this post deals with the recent loss of one of the great statisticians of our time - Theodore (Ted) W. Anderson.

Ted passed away on 17 September of this year, at the age of 98.

I'm hardly qualified to discuss the numerous, path-breaking, contributions that Ted made as a statistician. You can read about those in De Groot (1986), for example.

However, it would be remiss of me not to devote some space to reminding readers of this blog about the seminal contributions that Ted Anderson made to the development of econometrics as a discipline. In one of the "ET Interviews", Peter Phillips talks with Ted about his career, his research, and his role in the history of econometrics.  I commend that interview to you for a much more complete discussion than I can provide here.

(See this post for information about other ET Interviews).

Ted's path-breaking work on the estimation of simultaneous equations models, under the auspices of the Cowles Commission, was enough in itself to put him in the Econometrics Hall of Fame. He gave us the LIML estimator, and the Anderson and Rubin (1949, 1950) papers are classics of the highest order. It's been interesting to see those authors' test for over-identification being "resurrected" recently by a new generation of econometricians. 

There are all sorts of other "snippets" that one can point to as instances where Ted Anderson left his mark on the history and development of econometrics.

For instance, have you ever wondered why we have so many different tests for serial independence of regrsssion errors? Why don't we just use the uniformly most powerful (UMP) test and be done with it? Well, the reason is that no such test (against the alternative of a first-oder autoregresive pricess) exists.

That was established by Anderson (1948), and it led directly to the efforts of Durbin and Watson to develop an "approximately UMP test" for this problem.

As another example, consider the "General-to-Specific" testing methodology that we associate with David Hendry, Grayham Mizon, and other members of the (former?) LSE school of thought in econometrics. Why should we "test down", and not "test up" when developing our models? In other words, why should we start with the most  general form of the model, and then successively test and impose restrictions on the model, rather than starting with a simple model and making it increasingly complex? The short answer is that if we take the former approach, and "nest" the successive null and alternative hypotheses in the appropriate manner, then we can appeal to a theorem of Basu to ensure that the successive test statistics are independent. In turn, this means that we can control the overall significance level for the set of tests to what we want it to be. In contrast, this isn't possible if we use a "Simple-to-General" testing strategy.

All of this spelled out in Anderson (1962) in the context of polynomial regression, and is discussed further in Ted's classic time-series book (Anderson, 1971). The LSE school referred to this in promoting the "General-to-Specific" methodology.

Ted Anderson published many path-breaking papers in statistics and econometrics and he wrote several books - arguably, the two most important are Anderson (1958, 1971). He was a towering figure in the history of econometrics, and with his passing we have lost one of our founding fathers.

References

Anderson, T.W., 1948. On the theory of testing serial correlation. Skandinavisk Aktuarietidskrift, 31, 88-116.

Anderson, T.W., 1958. An Introduction to Multivariate Statistical Analysis. WIley, New York (2nd. ed. 1984).

Anderson, T.W., 1962. The choice of the degree of a polynomial regression as a multiple decision problem. Annals of Mathematical Statistics, 33, 255-265.

Anderson, T.W., 1971. The Statistical Analysis of Time Series. Wiley, New York.

Anderson, T.W. & H. Rubin, 1949. Estimation of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 20, 46-63.

Anderson, T.W. & H. Rubin, 1950. The asymptotic properties of the parameters of a single equation in a complete system of stochastic equations. Annals of Mathematical Statistics, 21,570-582.

De Groot, M.H., 1986. A Conversation with T.W. Anderson: An interview with Morris De Groot. Statistical Science, 1, 97–105.

© 2016, David E. Giles

Vintage Years in Econometrics - The 1950's

Following on from my earlier posts about vintage years for econometrics in the 1930's and 1940's, here's my run-down on the 1950's.

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."

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.

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

What's Your Favourite Estimator?

It's interesting to dwell on the popularity of different estimators that econometricians use. Some estimators are "in vogue" for a period, and then give way to others as new developments come along. Different topics have captured the attention of theoreticians and practitioners alike at different times in history.

Here's a Google Ngram showing the extent to which some familiar estimators for simultaneous equations models have been mentioned in books since 1960:

Not too surprisingly, good old OLS just goes on and on:

I was going to include the GMM estimator in these plots, but this acronym has meanings other than the obvious one that comes to mind. So, the results would have been misleading. To be safe, let's use the full phrase Generalized Method of Moments and allow for case sensitivity:

Interestingly, the phrase appeared in some books before the publication of Hansen's classic 1982 paper.

© 2013, David E. Giles

Assessing Heckman's Two-Step Estimator

Good survey papers are worth their weight in gold. Reading and digesting a thoughtful, constructive, and well-researched survey can save you a lot of work. It can also save you from making poor choices in your own research, or even from "re-inventing the wheel".

For these reasons, The Journal of Economic Surveys is a great resource. Over the years it has published some really fine peer-reviewed survey articles, many of which I've benefited from personally.

Another piece of good news is that Wiley (the journal's publisher) makes a number of the most highly-cited articles available for free.