The Coming Storm

The role of time-series statistics / econometrics in climate analyses is expanding (e.g., here).  Related -- albeit focusing on shorter-term meteorological aspects rather than longer-term climatological aspects -- it's worth listening to Michael Lewis' latest, The Coming Storm.  (You have to listen rather than read, as it's only available as an audiobook, but it's only about two hours.)  It's a fascinating story, well researched and well told by Lewis, just as you'd expect.  There are lots of interesting insights on (1) the collection, use, and abuse of public weather data, including ongoing, ethically-dubious, and potentially life-destroying attempts to privatize public weather data for private gain, (2) the clear and massive improvements in weather forecasting in recent decades, (3) behavioral aspects of how best to communicate forecasts so people understand them, believe them, and take appropriate action before disaster strikes. 

Climate Change and NYU Volatility Institute

There is little doubt that climate change -- tracking, assessment, and hopefully its eventual mitigation -- is the burning issue of our times. Perhaps surprisingly, time-series econometric methods have much to offer for weather and climatological modeling (e.g., here), and several econometric groups in the UK, Denmark, and elsewhere have been pushing the agenda forward.

Now the NYU Volatility Institute is firmly on board. A couple months ago I was at their most recent annual conference, "A Financial Approach to Climate Risk", but it somehow fell through the proverbial (blogging) cracks. The program is here, with links to many papers, slides, and videos. Two highlights, among many, were the presentations by Jim Stock (insights on the climate debate gleaned from econometric tools, slides here) and Bob Litterman (an asset-pricing perspective on the social cost of climate change, paper here). A fine initiative!

Time-Series Econometrics and Climate Change

It's exciting to see time series econometrics contributing to the climate change discussion.  

Check out the upcoming CREATES conference, "Econometric Models of Climate Change", here.

Here are a few good examples of recent time-series climate research, in chronological order.  (There are many more.  Look through the reference lists, for example, in the 2016 and 2017 papers below.)

Jim Stock et al. (2009) in Climatic Change.

Pierre Perron et al. (2013) in Nature.

Peter Phillips et al. (2016) in Nature.

Proietti and Hillebrand (2017), forthcoming in Journal of the Royal Statistical Society.

Climatology and Predictive Modeling

A notice about this paper just arrived.

Climate Engineering Economics

Garth Heutel, Juan Moreno-Cruz, Katharine Ricke

NBER Working Paper No. 21711
Issued in November 2015
NBER Program(s):   EEE 

Very cool, I thought. So I clicked on the EEE above, to see more systematically what the NBER's Environmental and Energy Economics group is doing these days. In general it has a very interesting list, and in particular it has an interesting list from a predictive modeling viewpoint. Check this, for example:

Modeling Uncertainty in Climate Change: A Multi-Model Comparison

Kenneth Gillingham, William D. Nordhaus, David Anthoff, Geoffrey Blanford, Valentina Bosetti, Peter Christensen, Haewon McJeon, John Reilly, Paul Sztorc

NBER Working Paper No. 21637
Issued in October 2015
NBER Program(s):   EEE 

The economics of climate change involves a vast array of uncertainties, complicating both the analysis and development of climate policy. This study presents the results of the first comprehensive study of uncertainty in climate change using multiple integrated assessment models. The study looks at model and parametric uncertainties for population, total factor productivity, and climate sensitivity. It estimates the pdfs of key output variables, including CO2 concentrations, temperature, damages, and the social cost of carbon (SCC). One key finding is that parametric uncertainty is more important than uncertainty in model structure. Our resulting pdfs also provide insights on tail events.

There's lots of great stuff in GNABBCMRS. (Sorry for the tediously-long acronym.) Among other things, it is correct in noting that "It is conceptually clear that the ensemble approach is an inappropriate measure of uncertainty of outcomes," and it takes a much broader approach. [The "ensemble approach" means different things in different meteorological / climatological contexts, but in this paper's context it means equating forecast error uncertainty with the dispersion of point forecasts across models.] The fact is that point forecast dispersion and forecast uncertainty are very different things. History is replete with examples of tight consensuses that turned out to be wildly wrong.

Unfortunately, however, the "ensemble approach" remains standard in meteorology / climatology. The standard econometric/statistical taxonomy, in contrast, includes not only model uncertainty, but also parameter uncertainty and innovation (stochastic shock) uncertainty. GNABBCMRS focus mostly on parameter uncertainty vs. model uncertainty and find that parameter uncertainty is much more important. That's a major advance.

But more focus is still needed on the third component of forecast error uncertainty, innovation uncertainty. The deterministic Newton / Lorenz approach embodied in much of meteorology / climatology needs thorough exorcising. I have long believed that traditional time-series econometric methods have much to offer in that regard.