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."

March Reading List

Now is a good time to catch up on some Econometrics reading. Here are my suggestions for this month:

• Carrasco, M. and R. Kotchoni, 2016. Efficient estimation using the characteristic function. Econometric Theory, in press.
• Chambers, M. J., 2016. The estimation of continuous time models with mixed frequency data. Discussion Paper No. 777, Department of Economics, University of Essex.
• Cuaresma, J. C., M. Feldkircher, and F. Huber, 2016. Forecasting with global vector autoregressive models: A Bayesian approach. Journal of Applied Econometrics, in press.
• Hendry, D., 2016. Deciding between alternative approaches in macroeconomics. Discussion Paper No. 778, Department of Economics, University of Oxford.
• Reed, W. R., 2016. Univariate unit root tests perform poorly when data are cointegrated. Working Paper No. 1/2016, Department of Economics and Finance, University of Canterbury.

© 2016, David E. Giles

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

The Indiana Jones of Economics

All students of economics have heard about The Phillips Curve in one of its forms or another. The Phillips Curve is named after A. W. H. (Bill) Phillips, a remarkable New Zealander who made a number of fundamental contributions. His work, undertaken largely at the London School of Economics, dealt with stabilization policy, and modelling in continuous time, to name just two topics.

Bill Phillips was quite a character, and his varied life has been amply documented in various places. His entry in Wikipedia is a useful starting point, and the memorial piece written in 1978 by one of my former teachers, Brian Easton, is also a "must read" item.

Some time ago, I wrote about Bill in a post titled, "A Moniacal Economist", in reference to his famous MONIAC machines. These were hydraulic analogue computers that could be used to demonstrate the workings of the macro-economy.

In February of this year, the BBC Radio aired a piece about Bill Phillips, titled "The India Jones of Economics". In this 14-minute broadcast, Tim Hartford provides an interesting commentary of Bill's life and contributions to our discipline. You can download the broadcast - it's Episode 4, 6 February 2013 - from here.

More about Bill Phillips at a later date..............

© 2013, David E. Giles

What I Learned Last Week

Somewhat to my surprise, last month I got a great response to my post, "My Must-Read List" (HT's to Mark Thoma & Tyler Cowen). This past week I learned a lot by reading some terrific new papers on a variety of econometrics topics. Here they are, with some commentary, and in no particular order: