Extreme Weather and the Macroeconomy

Check out the nice new paper here, by Hee Soo Kim, Christian Matthes, and Toan Phan.  Clever and nicely-controlled use of the Actuaries Climate Index (ACI) in an Auerbach-Gorodnichenko (AG, 2012) style smooth-transition VAR. Much bigger effects of extreme weather in more recent years! I look forward to learning/thinking more about assumptions embedded in the AG framework, and the role that they may play in conjunction with the new ACI data in producing the result.

Interactive Climate Change Simulator

Check out En-ROADS.  Looks intuitive and easy to run, yet quite detailed.  Nice!  (Confusing name though.  What does it mean?)

Hsiang Chosen for National Climate Assessment

On August 18th, the U.S. Global Change Research Program announced that Climate Impact Lab Co-Director Solomon Hsiang will serve as chapter lead for the Economics chapter of the Fifth National Climate Assessment (NCA5). 

Chapter leads will be responsible for building diverse author teams and leading the development of chapter content. NCA5 is expected to be released in late 2023.

NCA5 is a very big deal, and Sol is the perfect choice.

Hsiang is the Chancellor's Professor of Public Policy and Director of the Global Policy Laboratory at the University of California, Berkeley. His research combines data with mathematical models to understand how society and the environment influence one another. In particular, he focuses on how policy can encourage economic development while managing the global climate. His research has been published in Nature, Science, and the Proceedings of the National Academy of Sciences.

Unconditional and Conditional Density Forecast Calibration

I have been reading a new paper by Tilman Gneiting and Johannes Resin (GR2021) -- deep and thought-provoking as always -- "Regression Diagnostics meets Forecast Evaluation:  Conditional Calibration, Reliability Diagrams, and Coefficient of Determination."

Their paper pushes in a variety of interesting ways toward aspects of conditional as opposed to merely unconditional (uniform probability transform, or PIT) density forecast calibration.    

That's largely what the iid part of the Diebold-Gunther-Tay (DGT 1998) result ("correct conditional calibration implies PIT ~ iid U(0,1)") is about, and DGT emphasize and illustrate that one needs to check not just the condition for correct unconditional calibration (PIT ~ U(0,1)), but rather the joint condition PIT ~ iid U(0,1).  

I want to understand more about the GR2021 results in their relation to the iid part of the DGT1998 PIT ~ iid U(0,1) result.      

Some relevant history and perspective are at https://fxdiebold.blogspot.com/2018/06/peter-christoffersen-and-density.html.

The GR2021 paper and code are at:

https://urldefense.com/v3/__https://arxiv.org/abs/2108.03210__;!!IBzWLUs!BtUAeIBxhkYOch-aPxSnALk9MfYczGKu4G37CXTTHnimafIb85ov4cY12xVEYhicRaVT$

https://urldefense.com/v3/__https://github.com/resinj/replication_GR21__;!!IBzWLUs!BtUAeIBxhkYOch-aPxSnALk9MfYczGKu4G37CXTTHnimafIb85ov4cY12xVEYsNE2Kt1$

Climate change and financial risk

 Climate change and financial risk. The SF Fed's virtual seminars on climate economics

The Intergovernmental Panel on Climate Change has just published its sixth report, warning of “unprecedented” effects and “irreversible” damage. For more than a year, the Federal Reserve Bank of San Francisco has been running its Virtual Seminar on Climate Economics to help economists estimate, model, and propose policies to mitigate the extent of the crisis. Glenn Rudebusch of the FRBSF tells Tim Phillips that “the economics profession as a whole has been slow to grapple with climate change”, and so the seminars are bringing fresh, interdisciplinary research to a wider audience.

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