Including More History in Your Econometrics Teaching

If you follow this blog (or if you look at the "History of Econometrics" label in the word cloud in the right side-bar), you'll know that I have more than a passing interest in the history of our discipline. There's so much to be learned from this history. Among other things, we can gain insights into why certain methods became popular, and we can reduce the risk of repeating earlier mistakes!

When I was teaching I liked to inject a few historical facts/anecdotes/curiosities into my classes. I think that this brought the subject matter to life a little. The names behind the various theorems, tests, and estimators are those of real people, after all.

There are some excellent books on the history of econometrics, including those by Epstein (1987), Morgan (1990), and De Marchi and Gilbert (1991). (Also, see the short piece by Stephen Pollock, 2014.)

However, I think that we could do more in terms of making material about this history accessible to our students.

The Statistics community has gone much further in this direction, and we might take note of this.

The other day, Amanda Golbeck posted some very helpful links on the American Statistical Association's "History of Statistics Interest Group" community noticeboard.

Here's her posting in its entirety - and don't miss the first of her links:

"Why not include more history in your teaching? The History of Statistics Interest Group library has a collection of Activities for Classes: community.amstat.org/historyofstats/ourlibrary/...

We are pleased to let you know that Bob Rosenfeld has created 13 history of probability and statistics teaching modules, and he has kindly made them available for you to use in your classes! We hope you will find them to be useful.

Reading and Exercises on the History of Probability from the Vermont Mathematics Initiative, Bob Rosenfeld

• Pre-history to 1600 (PDF)
• 17th Century France (PDF)
• Jacob Bernoulli - Law of Large Numbers (PDF)
• Inverse Probability - Thomas Bayes (PDF)
• Laplace (PDF)

Reading and Exercises on the History of Statistics from the Vermont Mathematics Initiative, Bob Rosenfeld

• John Graunt and the Bills of Mortality (PDF)
• Origin of the Normal Curve (PDF)
• Origins of Graphs in Statistics (PDF)
• Fitting models to data - the Path to Least Squares (PDF)
• Statistics Moves from Physical to Social Sciences (PDF)
• Correlation - Francis Galton (PDF)
• t-Distribution and Gosset (PDF)
• Fisher and Design of Experiments (PDF)"

(Bob Rosenfeld was former Co-Director for Statistics and School-Based Research at the Vermont Mathenatics initiative, and the author of a number of books on the teaching of statistics to K-8 students. D.G.)

Most of Bob Rosenfeld's pieces are directly relevant to econometrics students. It would be nice to see more material about the history of our discipline that could be incorporated into introductory econometrics courses.

References 

De Marchi, N. & C. Gilbert, 1990. History and Methodology of Econometrics. Oxford University Press, Oxford.

Epstein, R. J. 1987. A History of Econometrics. North-Holland, Amsterdam.

Morgan, M. S., 1991. The History of Econometric Ideas. Cambridge University Press, Cambridge.

Pollock, D. S. G., 2014. Econometrics - An historical guide for the uninitiated. Working Paper No. 14/05, Department of economics, University of Leicester.

© 2019, David E. Giles

July Reading

This month my reading list is a bit different from the usual one. I've taken a look back at past issues of Econometrica and Journal of Econometrics, and selected some important and interesting papers that happened to be published in July issues of those journals.

Here's what I came up with for you:

• Aigner, D., C. A. K. Lovell, & P. Schmidt, 1977. Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6, 21-37.
• Chow, G. C., 1960. Tests of equality between sets of coefficients in two linear regressions. Econometrica, 28, 591-605.
• Davidson, R. & J. G. MacKinnon, 1984. Convenient specification tests for logit and probit models. Journal of Econometrics, 25, 241-262.
• Dickey, D. A. & W. A. Fuller, 1981. Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49, 1057-1072.
• Granger, C. W. J. & P. Newbold,  1974. Spurious regressions in econometrics. Journal of Econometrics, 2, 111-120.
• Sargan, J. D., 1961. The maximum likelihood estimation of economic relationships with autoregressive residuals. Econometrica, 29, 414-426. 

© 2019, David E. Giles

More Tributes to Clive Granger

As a follow-up to my recent post, "Clive Granger Special Issue", I received an email from Eyüp Çetin (Editor of the European Journal of Pure and Applied Mathematics).

Eyüp kindly pointed out that "......... actually, we published the first special issue dedicated to his memory exactly on 27 May 2010, the first anniversary of his passing at https://www.ejpam.com/index.php/ejpam/issue/view/11 

We think this was the first special issue dedicated to his memory in the world. The Table of Contents may be found here https://www.ejpam.com/index.php/ejpam/issue/view/11/showToc .

Another remarkable point that we also published some personal and institutional tributes and some memorial stories for Sir Granger that never appeared elsewhere before at 

https://www.ejpam.com/index.php/ejpam/article/view/805 .

Some institutions such as Royal Statistical Society, Japan Statistical Society and University of Canterbury have sent their tributes to this special volume." 

© 2019, David E. Giles

SHAZAM!

This past weekend the new movie, Shazam, topped the box-office revenue list with over US$53million - and this is it's first weekend since being released.

Not bad!

Of course, in the Econometrics World, we associate the word, SHAZAM, with Ken White's famous computing package, which has been with us since 1977. 

Ken and I go way back. A few years ago I had a post about the background to the SHAZAM package. In that post I explained what the acronym "SHAZAM" stands for. If you check it out you'll see why it's timely for you to know these important historical facts!

And while you're there, take a look at the links to other tales that illustrate Ken's well-known wry sense of humour.

© 2019, David E. Giles

Some April Reading for Econometricians

Here are my suggestions for this month:

• Hyndman, R. J., 2019. A brief history of forecasting competitions. Working Paper 03/19, Department of Econometrics and Business Statistics, Monash University.
• Kuffner, T. A. & S. G. Walker, 2019. Why are p-values controversial?. American Statistician, 73, 1-3.
• Sargan, J. D.,, 1958. The estimation of economic relationships using instrumental variables. Econometrica, 26, 393-415. (Read for free online.)  
• Sokal, A. D., 1996. Transgressing the boundaries: Towards a trasnformative hermeneutics of quantum gravity. Social Text, 46/47, 217-252.
• Zeng, G. & Zeng, E., 2019. On the relationship between multicollinearity and separation in logistic regression. Communications in Statistics - Simulation and Computation, published online.
• Zhang, X., S. Paul, & Y-G. Yang, 2019. Small sample bias correction or bias reduction? Communications in Statistics - Simulation and Computation, published online.

© 2019, David E. Giles

An Archival History of the Econometric Society

For those of you who have an interest in the history of Econometrics as a discipline - that's all of you, right (?) - there's a fascinating collection of material available at The Econometric Society: An Archival History.

As the name suggests, this repository relates to the Econometric Society and the journal Econometrica. It contains all sorts of fascinating facts, correspondence, and the like.

© 2018, David E. Giles

April Reading

Very belatedly, here is my list of suggested reading for April:

• Biørn, E., 2017. Identification, instruments, omitted variables, and rudimentary models: Fallacies in the "experimental approach" to econometrics. Memorandum No. 13/2017, Department of Economics, Oslo University.
• Chambers, M. J., and M. Kyriacou, 2018. Jackknife bias reduction in the presence of a near-unit root. Econometrics, 6, 11.
• Derryberry, D., K. Aho, J. Edwards, and T. Peterson, 2018. Model selection and regression t-statistics. American Statistician, in press.
• Mitchell, J., D. Robertson, and S. Wright, 2018. R² bounds for predictive models: What univariate properties tell us about multivariate predictability. Journal of Business and Economic Statistics, in press. (Free download here.)
• Parker, T., 2017. Finite-sample distributions of the Wald, likelihood ratio, and Lagrange multiplier test statistics in the classical linear model. Communications in Statistics - Theory and Methods, 46, 5195-5202.
• Troster, V., 2018. Testing Granger-causality in quantiles. Econometric Reviews, 37, 850-866.

© 2018, David E. Giles

March Reading List

• Annen, K. & S. Kosempel, 2018. Why aid-to-GDP ratios? Discussion Paper 2018-01, Department of Economics and Finance, University of Guelph.
• Conover, W. J., A. J. Guerrero-Serrano, & V. G. Tercero-Gomez, 2018. An update on 'a comparative study of tests for homogeneity of variance'. Journal of Statistical Computation and Simulation, online.
• Foroni, C., M. Marcellino, & D. Stevanović, 2018. Mixed frequency models with MA components. Discussion Paper  No. 02/2018, Deutsche Bundesbank.
• Sen, A., 2018. Lagrange multiplier unit root test in the presence of a break in the innovation variance. Communications in Statistics - Theory and Methods, 47, 1580-1596.
• Stewart, K. G., 2018. Suits' watermelon model: The missing simultaneous equations empirical example. Mimeo., Department of Economics, University of Victoria.
• Weigt, T. & B. Wilfling, 2018. An approach to increasing forecast-combination accuracy through VAR error modeling. Paper 68/2018, Department of Economics, University of Münster.

© 2018, David E. Giles

When Everything Old is New Again

Some ideas are so good that they keep re-appearing again and again. In other words, they stand the test of time, and prove to be useful in lots of different contexts – sometimes in situations that we couldn’t have imagined when the idea first came to light.

This certainly happens in econometrics, and here are just a few examples that come to mind.

Read Some Econometrics this Month!

There are no April Fool's tricks in the following list of suggestions. 😐

• Antoine, B. and E. Renault, 2017. On the relevance of weaker instruments. Econometric Reviews, online.
• Golan, A. and A. Ullah, 2017. Interval estimation: An information theoretic approach. Econometric Reviews, online.
• Hendry, D. F. and P. C. B. Phillips, 2017. John Denis Sargan at the London School of Economics. Cowles Foundation Discussion Paper No. 2082, Yale University.
• Ozonur, D., H. T. K. Akdur, & H. Bayrak, 2016. Comparisons of tests of distributional assumption in Poisson regression model. Communications in Statistics - Simulation and Computation, online.
• Vijayamohanan, P. N., 2017. How do you interpret your regression coefficients? MPRA Paper No. 76867.
• Witmer, J., 2017. Bayes and MCMC for undergraduates. American Statistician, online.

© 2017, David E. Giles

In Praise of Two Giants of Econometrics

Two giants in our field, now deceased, are celebrated in recent Working Papers by Peter Phillips and Timo Teräsvirta.

Peter's paper is titled, "Tribute to T. W. Anderson", is in an issue of Econometric Theory that also includes ted's last published research paper.

Timo's paper, which will be appearing in The Journal of Pure and Applied Mathematics, "Sir Clive Granger's contributions to nonlinear time series and econometrics".

Both papers are essential reading, whether you have a particular interest in the history of econometrics; of if you are a younger researcher who wants to understand the building blocks of our discipline.

© 2017, David E. Giles

In Honour of Peter Schmidt

The latest issue of Econometric Reviews (Vol 36, Nos. 1-3) is devoted to papers that have been assembled to honour Peter Schmidt, of Michigan State University. Peter's contributions to econometrics have been outstanding, and it's great to see his work celebrated in this way.

In the abstract to their introduction to this collection Essie Maasoumi and Robin Sickles comment as follows:

"Peter Schmidt has been one of its best-known and most respected econometricians in the profession for four decades. He has brought his talents to many scholarly outlets and societies, and has played a foundational and constructive role in the development of the field of econometrics. Peter Schmidt has also served and led the development of Econometric Reviews since its inception in 1982. His judgment has always been fair, informed, clear, decisive, and constructive. Respect for ideas and scholarship of others, young and old, is second nature to him. This is the best of traits, and Peter serves as an uncommon example to us all. The seventeen articles that make up this Econometric Reviews Special Issue in Honor of Peter Schmidt represent the work of fifty of the very best econometricians in our profession. They honor Professor Schmidt’s lifelong accomplishments by providing fundamental research work that reflects many of the broad research themes that have distinguished his long and productive career. These include time series econometrics, panel data econometrics, and stochastic frontier production analysis."

I hope that you get a chance to read the papers in this issue of Econometric Reviews.

© 2017, David E. Giles

Vintage Years in Econometrics - The 1970's

Continuing on from my earlier posts about vintage years for econometrics in the 1930's, 1940's, 1950's, 1960's, here's my tasting guide for the 1970's.

Once again, 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."

Explaining the Almon Distributed Lag Model

In an earlier post I discussed Shirley Almon's contribution to the estimation of Distributed Lag (DL) models, with her seminal paper in 1965.

That post drew quite a number of email requests for more information about the Almon estimator, and how it fits into the overall scheme of things. In addition, Almon's approach to modelling distributed lags has been used very effectively more recently in the estimation of the so-called MIDAS model. The MIDAS model (developed by Eric Ghysels and his colleagues - e.g., see Ghysels et al., 2004) is designed to handle regression analysis using data with different observation frequencies. The acronym, "MIDAS", stands for "Mixed-Data Sampling". The MIDAS model can be implemented in R, for instance (e.g., see here), as well as in EViews. (I discussed this in this earlier post.)

For these reasons I thought I'd put together this follow-up post by way of an introduction to the Almon DL model, and some of the advantages and pitfalls associated with using it.

Let's take a look.

More on the History of Distributed Lag Models

In a follow-up to my recent post about Irving Fisher's contribution to the development of distributed lag models,  Mike Belongia emailed me again with some very interesting material. He commented:

"While working with Peter Ireland to create a model of the business cycle based on what were mainstream ideas of the 1920s (including a monetary policy rule suggested by Holbrook Working), I ran across this note on Fisher's "short cut" method to deal with computational complexities (in his day) of non-linear relationships. 

I look forward to your follow-up post on Almon lags and hope Fisher's old, and sadly obscure, note adds some historical context to work on distributed lags."

It certainly does, Mike, and thank you very much for sharing this with us.

The note in question is titled, "Irving Fisher: Pioneer on distributed lags", and was written by J.N.M Wit (of the Netherlands central bank) in 1998. If you don't have time to read the full version, here's the abstract:

"The theory of distributed lags is that any cause produces a supposed effect only after some lag in time, and that this effect is not felt all at once, but is distributed over a number of points in time. Irving Fisher initiated this theory and provided an empirical methodology in the 1920’s. This article provides a small overview."

Incidentally, the paper co-authored with Peter Ireland that Mike is referring to it titled, "A classical view of the business cycle", and can be found here.
© 2016, David E. Giles

Irving Fisher & Distributed Lags

Some time back, Mike Belongia (U. Mississippi) emailed me as follows: 

"I enjoyed your post on Shirley Almon;  her name was very familiar to those of us of a certain age.

With regard to your planned follow-up post, I thought you might enjoy the attached piece by Irving Fisher who, in 1925, was attempting to associate variations in the price level with the volume of trade.  At the bottom of p. 183, he claims that "So far as I know this is the first attempt to distribute a statistical lag" and then goes on to explain his approach to the question.  Among other things, I'm still struck by the fact that Fisher's "computer" consisted of his intellect and a pencil and paper."

The 1925 paper by Fisher that Mike is referring to can be found here. Here are pages 183 and 184:

Thanks for sharing this interesting bit of econometrics history, Mike. And I haven't forgotten that I promised to prepare a follow-up post on the Almon estimator!

© 2016, David E. Giles

Monte Carlo Simulation Basics, I: Historical Notes

Monte Carlo (MC) simulation provides us with a very powerful tool for solving all sorts of problems. In classical econometrics, we can use it to explore the properties of the estimators and tests that we use. More specifically, MC methods enable us to mimic (computationally) the sampling distributions of estimators and test statistics in situations that are of interest to us. In Bayesian econometrics we use this tool to actually construct the estimators themselves. I'll put the latter to one side in what follows.

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

The ANU Tapes of the British (Econometrics) Invasion

As far as I know, the Beatles never performed at the Australian National University (the ANU). But the "fab. three" certainly did, and we're incredibly lucky to have the visual recordings to prove it!

Stan Hurn (Chair of the Board of the National Centre for Econometric Research, based in the Business School at the Queensland University of Technology) contacted me recently about a fantastic archive that has been made available.

The Historical Archive at the NCER now includes the digitized versions of the movies that were made in the 1970's and 1980's when various econometricians from the London School of Economics visited and lectured at the ANU. Specifically, eight lectures by Grayham Mizon, five by Ken Wallis, and a further eight lectures by Denis Sargan can be viewed here.

I was on faculty at Monash University at the time of these visits (and that of David Hendry - so I guess the fab. four of of the LSE did actually make it). I recall them well because the visitors also gave seminars in our department while they were in Australia. 

Before you view the lectures - and I really urge you to do so - it's essential that you read the background piece, "The ANU Tapes: A Slice of History", written by Chris Skeels. (Be sure to follow the "Read more" link, and read the whole piece.) As it happens, Chris was a grad. student in our group at Monash back in the day, and his backgrounder outlines a remarkable story of how the tapes were saved.

Kudos to Stan and his colleagues for putting this archive together. And double kudos to Chris Skeels for having the foresight, energy, and determination to ensure that we're all able to share these remarkable lectures.

Thank you both!

© 2016, David E. Giles

Econometrics Reading List for June

Here's some suggested reading for the coming month:

• Backhouse, R. and B. Cherrier, 2016. 'It's  computerization, stupid!' The spread of computers and the changing roles of theoretical and applied economics.
• Castle, J. L., M. P. Clements, and D. F. Hendry, 2016. An overview of forecasting facing breaks. Discussion Paper No. 779, Department of Economics, University of Oxford.
• Constantini. M. and A. Sen, 2016. A simple testing procedure for unit root and model specification. Computational Statistics and Data Analysis, 102, 37-54.
• Lutkepohl, H., A. Staszewska-Bystrova, and P. Winker, 2016. Calculating joint confidence bands for impulse response functions using highest density regions. SFB 649 Discussion Paper 2016-017.
• Valadkhani, A., R. Smyth, and B. Mahoney, 2016. Asymmetric causality between Australian inbound and outbound migration tourism flows. Applied Economics, in press.
• Webel, K., 2016. A data-driven selection of an appropriate seasonal adjustment approach. Discussion Paper No. 07/2016, Deutsche Bundesbank.

© 2016, David E. Giles