Friday, December 18, 2015

Holiday Haze

File:Happy Holidays (5318408861).jpg


Your dedicated blogger is about to vanish in the holiday haze, returning in the new year. Meanwhile, all best wishes for the holidays.








[Photo credit:  Public domain, by Marcus Quigmire, from Florida, USA (Happy Holidays  Uploaded by Princess Mérida) [CC-BY-SA-2.0 (http://creativecommons.org/licenses/by-sa/2.0)], via Wikimedia Commons]




Monday, December 14, 2015

Sunday, December 13, 2015

Superforecasting



A gratis copy of Philip Tetlock and Dan Gardner's new book, Superforecasting, arrived a couple months ago, just before it was published. It's been sitting on my desk until now. With a title like "Superforcasting," perhaps I subconsciously thought it would be pop puffery and delayed looking at it. If so, I was wrong. It's a winner.

Superforecasting is in the tradition of Nate Silver's The Signal and the Noisebut whereas Silver has little expertise (except in politics, baseball and poker, which he knows well) and goes for breadth rather than depth, Tetlock has significant expertise (his own pioneering research, on which his book is built) and goes for depth. Tetlock's emphasis throughout is on just one question: What makes good forecasters good?

Superforecasting is mostly about probabilistic event forecasting, for events much more challenging than those that we econometricians and statisticians typically consider, and for which there is often no direct historical data (e.g., conditional on information available at this moment, what is the probability that Google files for bankruptcy by December 31, 2035?). Nevertheless it contains many valuable lessons for us in forecast construction, evaluation, combination, updating, etc.

You can expect several posts on aspects of Superforecasting in the new year as I re-read it. For now I just wanted to bring it to your attention in case you missed it. Really nice.

Thursday, December 10, 2015

Long Memory Stochastic Volatility

Check out Mark Jensen's new paper.  Long memory is a key feature of realized high-frequency asset-return volatility, yet it remains poorly understood. Jensen's approach may help change that. Of particular interest are: (1) its ability to handle seamlessly d in [0, 1[, despite the fact that the unconditional variance is infinite for d in ].5, 1[, and (2) closely related, the important role played by wavelets. 

Details:

Robust estimation of nonstationary, fractionally integrated, autoregressive, stochastic volatility

Date:
2015-11-01
By:
Jensen, Mark J. (Federal Reserve Bank of Atlanta)
Empirical volatility studies have discovered nonstationary, long-memory dynamics in the volatility of the stock market and foreign exchange rates. This highly persistent, infinite variance—but still mean reverting—behavior is commonly found with nonparametric estimates of the fractional differencing parameter d, for financial volatility. In this paper, a fully parametric Bayesian estimator, robust to nonstationarity, is designed for the fractionally integrated, autoregressive, stochastic volatility (SV-FIAR) model. Joint estimates of the autoregressive and fractional differencing parameters of volatility are found via a Bayesian, Markov chain Monte Carlo (MCMC) sampler. Like Jensen (2004), this MCMC algorithm relies on the wavelet representation of the log-squared return series. Unlike the Fourier transform, where a time series must be a stationary process to have a spectral density function, wavelets can represent both stationary and nonstationary pr! ocesses. As long as the wavelet has a sufficient number of vanishing moments, this paper's MCMC sampler will be robust to nonstationary volatility and capable of generating the posterior distribution of the autoregressive and long-memory parameters of the SV-FIAR model regardless of the value of d. Using simulated and empirical stock market return data, we find our Bayesian estimator producing reliable point estimates of the autoregressive and fractional differencing parameters with reasonable Bayesian confidence intervals for either stationary or nonstationary SV-FIAR models.
Keywords:
JEL:
URL:



New Elsevier: Good or Bad?


This just in from Elsevier.  Hardly my favorite firm, but still.  Does it resonate with you, for your own future publications? I am intrigued, insofar as it may actually have scientific value in disseminating research and helping people world-wide to see "seminars" that they wouldn't otherwise see. On the other hand, it would be more work, and the Elsevier implementation may be poor.  (Click below on  "View a sample presentation".  Can you find it?  I looked for five minutes and couldn't.  Maybe it's just me.)  Thoughts?


Elsevier
AudioSlides - explain your paper in your own words

Congratulations on the acceptance of your article Improving GDP Measurement: A Measurement-Error Perspective for publication in Journal of Econometrics. Now that your article is set to be published online, it is time to think about ways to promote your work and get your message across to the research community.
How would you like to present your research to a large audience, highlighting your main findings and articulating the relevance of your results in your own words? With AudioSlides, a new and free service by Elsevier, you can do exactly that!

The AudioSlides Authoring Environment* enables you to create an interactive presentation from your slides and add voice-over audio recordings, using only a web browser and a computer with a microphone. When it's ready, your presentation will be made available next to your published article on ScienceDirect. Click here to get started.

Benefits for authors and readers:
  • Promote your work and summarize your research in your own words
  • Support readers to quickly determine the relevance of your paper
  • Use a dedicated, easy-to-use website to create your AudioSlides presentation
  • AudioSlides presentations can be embedded in other websites
  • AudioSlides presentations will be made available next to your published article on ScienceDirect (View a sample presentation)
  • Did we mention it's free?
We hope you share our enthusiasm about this new service. Thank you for your interest!
Yours sincerely,
Hylke Koers
Head of Content Innovation, STM Journals, Elsevier



*Note that AudioSlides presentations are limited to 5 minutes maximum and should be in English. We advise you to keep the slides limited in number and simple so that they can also be viewed at low resolution (the default width on ScienceDirect for the viewer application is 270 pixels).

AudioSlides presentation are not peer-reviewed, and will be made available with your published article without delay after you have finalized your presentation and completed the online copyright transfer form. See also the Terms & Conditions.

Further information, instructions and an FAQ are available
at http://www.elsevier.com/audioslides.

For questions regarding Audioslides please visit http://help.elsevier.com/app/answers/list/p/8828/c/9413.
Elsevier

Sunday, December 6, 2015

New Review of Forecasting at Bank of England

Check it out here. It's thorough and informative.  

It's interesting and unfortunate that even the Bank of England, the great "fan chart pioneer," produces density forecasts for only three of eleven variables forecasted (p. 15). In my view, the most important single forecasting improvement that the Bank of England -- and all central banks -- could implement is a complete switch from point to density forecast construction, evaluation and combination.

Wednesday, December 2, 2015

NYU "Five-Star" Conference 2015

Program with clickable papers here.  The amazing thing about Five-Star is that it actually works, and works well, year after year, despite the usually-disastrous fact that it involves coordination among universities. 

Eurostat Forecasting Competition Deadline Approaching

I have some serious reservations about forecasting competitions, at least as typically implemented by groups like Kaggle. But still they're useful and exciting and absolutely fascinating. Here's a timely call for participation, from Eurostat. (Actually this one is nominally for nowcasting, not forecasting, but in reality they're the same thing.) 

[I'm not sure why they're trying to shoehorn "big data" into it, except that it sounds cool and everyone wants to jump on the bandwagon. The winner is the winner, whether based on big data, small data, or whatever, and whether produced by an econometrician, a statistician, or a data scientist. I'm not even sure what "Big Data" means, or what a "data scientist" means, here or anywhere. (Standard stat quip: A data scientist is a statistician who lives in San Francisco.) End of rant.]


Big Data for Official Statistics Competition launched - please register by 10 January 2016

 

The Big Data for Official Statistics Competition (BDCOMP) has just been launched, and you are most welcome to participate. All details are provided in the call for participation:
Participation is open to everybody (with a few very specific exceptions detailed in the call).
In this first instalment of BDCOMP, the competition is exclusively about nowcasting economic indicators at national or European level.
There are 7 tracks in the competition. They correspond to 4 main indicators: Unemployment, HICP, Tourism and Retail Trade and some of their variants.
Usage of Big Data is encouraged but not mandatory. For a detailed description of the competition tasks, please refer to the call.

The authors of the best-performing submissions for each track will be invited to present their work at the NTTS 2017 conference (the exact award criteria can be found in the call).

The deadline for registration is 10 January 2016. The duration of the competition is roughly a year (including about a month for evaluation). For a detailed schedule of submissions, please refer to the call.

The competition is organised by Eurostat and has a Scientific Committee composed of colleagues from various member and observer organisations of the European Statistical System (ESS).

On the behalf of the BDCOMP Scientific Committee,

The BDCOMP organising team