The Overseas Development Institute Fellowship Scheme sends young postgraduate statisticians (and economists) to work in the public sectors of developing countries in Africa, the Caribbean and the Pacific on two-year contracts. This is a great way to develop skills and gain experience working within a developing country’s government. And you get to live in a fascinating place!
The application process for the 2016-2018 Fellowship Scheme is now open. Students are advised to apply before 17 December 2015 for a chance to be part of the ODI Fellowship Scheme.
- degree in statistics, economics, or a related field
- postgraduate degree qualification
- ability to commit to a two-year assignment
Application is via the online application form.
Read some first-hand experiences of current and former Fellows.
Today at the International Symposium on Forecasting, I announced the awards for the best paper published in the International Journal of Forecasting in the period 2012-2013.
We make an award every two years to the best paper(s) published in the journal. There is always about 18 months delay after the publication period to allow time for reflection, citations, etc. The selected papers are selected by vote of the editorial board. The best paper wins an engraved bronze plaque and US$1000. Any other awards are in the form of certificates. Continue reading →
I do not normally post job adverts, but this was very specifically targeted to “applied time series candidates” so I thought it might be of sufficient interest to readers of this blog. Continue reading →
I am a statistician, but I have worked in a department of predominantly econometricians for the past 17 years. It is a little like an Australian visiting the United States. Initially, it seems that we talk the same language, do the same sorts of things, and have a very similar culture. But the longer you stay there, the more you realise there are differences that run deep and affect the way you see the world.
Last week at my research group meeting, I spoke about some of the differences I have noticed. Coincidentally, Andrew Gelman blogged about the same issue a day later. Continue reading →
Last week my research group discussed Hal Varian’s interesting new paper on “Big data: new tricks for econometrics”, Journal of Economic Perspectives, 28(2): 3-28.
It’s a nice introduction to trees, bagging and forests, plus a very brief entree to the LASSO and the elastic net, and to slab and spike regression. Not enough to be able to use them, but ok if you’ve no idea what they are. Continue reading →
I’m tired of reading about tests for structural breaks and here’s why.
A structural break occurs when we see a sudden change in a time series or a relationship between two time series. Econometricians love papers on structural breaks, and apparently believe in them. Personally, I tend to take a different view of the world. I think a more realistic view is that most things change slowly over time, and only occasionally with sudden discontinuous change. Continue reading →
Last week, my research group discussed Galit Shmueli’s paper “To explain or to predict?”, Statistical Science, 25(3), 289-310. (See her website for further materials.) This is a paper everyone doing statistics and econometrics should read as it helps to clarify a distinction that is often blurred. In the discussion, the following issues were covered amongst other things.
- The AIC is better suited to model selection for prediction as it is asymptotically equivalent to leave-one-out cross-validation in regression, or one-step-cross-validation in time series. On the other hand, it might be argued that the BIC is better suited to model selection for explanation, as it is consistent.
- P-values are associated with explanation, not prediction. It makes little sense to use p-values to determine the variables in a model that is being used for prediction. (There are problems in using p-values for variable selection in any context, but that is a different issue.)
- Multicollinearity has a very different impact if your goal is prediction from when your goal is estimation. When predicting, multicollinearity is not really a problem provided the values of your predictors lie within the hyper-region of the predictors used when estimating the model.
- An ARIMA model has no explanatory use, but is great at short-term prediction.
- How to handle missing values in regression is different in a predictive context compared to an explanatory context. For example, when building an explanatory model, we could just use all the data for which we have complete observations (assuming there is no systematic nature to the missingness). But when predicting, you need to be able to predict using whatever data you have. So you might have to build several models, with different numbers of predictors, to allow for different variables being missing.
- Many statistics and econometrics textbooks fail to observe these distinctions. In fact, a lot of statisticians and econometricians are trained only in the explanation paradigm, with prediction an afterthought. That is unfortunate as most applied work these days requires predictive modelling, rather than explanatory modelling.
We have an exciting new initiative at Monash University with some new positions in business analytics. This is part of a plan to strengthen our research and teaching in the data science/computational statistics area. We are hoping to make multiple appointments, at junior and senior levels. These are five-year appointments, but we hope that the positions will continue after that if we can secure suitable funding. Continue reading →
There are several other blogs on forecasting that readers might be interested in. Here are seven worth following:
- No Hesitations by Francis Diebold (Professor of Economics, University of Pennsylvania). Diebold needs no introduction to forecasters. He primarily covers forecasting in economics and finance, but also xkcd cartoons, graphics, research issues, etc.
- Econometrics Beat by Dave Giles. Dave is a professor of economics at the University of Victoria (Canada), formerly from my own department at Monash University (Australia), and a native New Zealander. Not a lot on forecasting, but plenty of interesting posts about econometrics and statistics more generally.
- Business forecasting by Clive Jones (a professional forecaster based in Colorado, USA). Originally about sales and new product forecasting, but he now covers a lot of other forecasting topics and has an interesting practitioner perspective.
- Freakonometrics: by Arthur Charpentier (an actuary and professor of mathematics at the University of Quebec at Montreal, Canada). This is the most prolific blog on this list. Wide ranging and taking in statistics, forecasting, econometrics, actuarial science, R, and anything else that takes his fancy. Sometimes in French.
- No free hunch: the kaggle blog. Some of the most interesting posts are from kaggle competition winners explaining their methods.
- Energy forecasting by Tao Hong (formerly an energy forecaster for SAS, now a professor at UNC). He covers mostly energy forecasting issues and job postings.
- The official IIF blog. Conferences, jobs, member profiles, etc.