In Memoriam Peter Christoffersen

It brings me great sadness to report that Peter Christoffersen passed away this morning after a long and valiant struggle with cancer. (University of Toronto page here, personal page here.) He departed peacefully, surrounded by loving family. I knew Peter and worked closely with him for nearly thirty years. He was the finest husband, father, and friend imaginable. He was also the finest scholar imaginable, certainly among the leading financial economists and financial econometricians of his generation. I will miss him immensely, both personally and professionally.

10th ECB Workshop on Forecasting Techniques, Frankfurt

Starts now, program here. Looks like a great lineup. Most of the papers are posted, and the organizers also plan to post presentation slides following the conference. Presumably in future weeks I'll blog on some of the presentations.

Deep Neural Nets for Volatility Dynamics

There doesn't seem to be much need for nonparametric nonlinear modeling in empirical macro and finance. Not that lots of smart people haven't tried. The two key nonlinearities (volatility dynamics and regime switching) just seem to be remarkably well handled by tightly-parametric customized models (GARCH/SV and Markov-switching, respectively). 

But the popular volatility models are effectively linear (ARMA) in squares. Maybe that's too rigidly constrained. Volatility dynamics seem like something that could be nonlinear in ways much richer than just ARMA in squares. 

Here's an attempt using deep neural nets. I'm not convinced by the paper -- much more thorough analysis and results are required than the 22 numbers reported in the "GARCH" and "stocvol" columns of its Table 1 -- but I'm intrigued.

It's quite striking that neural nets, which have been absolutely transformative in other areas of predictive modeling, have thus far contributed so little in economic / financial contexts. Maybe the "deep" versions will change that, at least for volatility modeling. Or maybe not. 

Machines Learning Finance

FRB Atlanta recently hosted a meeting on "Machines Learning Finance". Kind of an ominous, threatening (Orwellian?) title, but there were lots of (non-threatening...) pieces. I found the surveys by Ryan Adams and John Cunningham particularly entertaining. A clear theme on display throughout the meeting was that "supervised learning" -- the main strand of machine learning -- is just function estimation, and in particular, conditional mean estimation. That is, regression. It may involve high dimensions, non-linearities, binary variables, etc., but at the end of the day it's still just regression. If you're a regular No Hesitations reader, the "insight" that supervised learning = regression will hardly be novel to you, but still it's good to see it disseminating widely.

Top 100 Economics Blogs

Check out the latest "Top 100 Economics Blogs" here. The blurb for No Hesitations (under "Sub-field Economic Blogs") is pretty funny, issuing a stern warning: 

His blog is primarily focused on statistics and econometrics, and is highly technical. Therefore, it is recommended for those with advanced knowledge of economics and mathematics.

In reality, and as I'm sure you'll agree if you're reading this, it's actually simple and intuitive! I guess it's all relative. Anyway the blurb does get this right: "It is especially recommended for those wanting to learn more about dynamic predictive modeling in economics and finance."

Quite apart from pros and cons of its No Hesitations blurb (surely of much more interest to me than to you...), the list provides an informative and timely snapshot of the vibrant economics blogosphere.

Monetary Policy and Global Spillovers

The Bank of Chile's latest Annual Conference volume, Monetary Policy and Global Spillovers: Mechanisms, Effects, and Policy Measures, is now out, here.  In addition to the research presented in the volume, I love the picture on its front cover. So peaceful.

Fourth Penn Quantitative Policy Workshop

Some years ago I blogged on the first Workshop on Quantitative Tools for Macroeconomic Policy Analysis hosted by the Penn Institute for Economic Research (PIER). We just completed the fourth! It was a great group as usual, with approximately 25 participants from around the globe, mostly economists at country central banks, ECB, etc. Some of the happy campers, along with yours truly, appear in the photo. You can find all sorts of information on the workshop site. Information / registration for the next Workshop (May 2019) will presumably be posted in fall. Please consider joining us, and tell your friends!

Pockets of Predictability

Some months ago I blogged on "Pockets of Predictability," here. The Farmer-Schmidt-Timmermann paper that I mentioned is now available, here.

Ghysels and Marcellino on Time-Series Forecasting

If you're teaching a forecasting course and want a good text, or if you're just looking for an informative and modern treatment, see Applied Economic Forecasting Using Time Series Methods, by Eric Ghysels and Massimilliano Marcellino. It will be published this week by Oxford University Press. It has a very nice modern awareness of Big Data with emphasis on reduced-rank structure, regularization methods -- LASSO appears as early as p. 23! -- , structural change, mixed-frequencies, etc. It's also very tastefully done in terms of what's included and what's excluded, emphasizing what's most important and de-emphasizing the rest. As regards non-linearity, for example, volatility dynamics and regime-switching are in, and most of the rest is out.

The History of Forecasting Competitions

Check out Rob Hyndman's "Brief History of Time Series Forecasting Competitions". I'm not certain whether the title's parallel to Hawking's Brief History of Time is intentional. At any rate, even if Hyndman's focus is rather more narrow than the origin and fate of the universe, his post is still fascinating and informative. Thanks to Ross Askanasi for bring it to my attention.