Statistical model selection with “Big Data”: Doornik & Hendry’s New Paper

The claim that causation has been ‘knocked off its pedestal’ is fine if we are making predictions in a stable environment but not if the world is changing …. or if we ourselves hope to change it. – Harford, 2014

Ten or fifteen years ago, big data sounded like the best thing ever in econometrics. When you spend your undergraduate career learning that (almost!) everything can be solved in classical statistics with more data, it sounds great. But big data comes with its own issues. No free lunch, too good to be true and your mileage really does vary.

In a big data set, statistical power isn’t the issue. You have power enough for just about everything. But that comes with problems of its own. The probability of a Type II error may be very high. In this context, it’s the possibility of falsely interpreting that a parameter estimate is significant when in fact it is not. The existence of spurious relationships are likely. Working out which ones are truly significant and those that are spurious is difficult. Model selection in the big data context is complex!

David Hendry is one of the powerhouses of modern econometrics and the fact that he is weighing into the big data model selection problem is a really exciting proposition. This week a paper was published with Jurgen Doornik in the Cogent Journal of Economics and Finance. You can find it here.

Doornik and Hendry propose a methodology for big data model selection. Big data comes in many varieties and in this paper, they consider only cross-sectional and time series data of “fat” structure: that is, more variables than observations. Their results generalise to other structures, but not always. Doornik and Hendry describe four key issues for big data model selection in their paper:

  • Spurious relationships
  • Mistaking correlations for causes
  • Ignoring sampling bias
  • Overestimating significance of results.

So, what are Doornik and Hendry’s suggestions for model selection in a big data context? Their approach has several pillars to the overall concept:

  1. They calculate the probabilities of false positives in advance. It’s long been possible in statistics to set the significance level to control multiple simultaneous tests. This is an approach taken in both ANOVA testing for controlling the overall significance level when testing multiple interactions and in some panel data approaches when testing multiple cross-sections individually. The Bonferroni inequality is the simplest of this family of techniques, though Doornik and Hendry are suggesting a far more sophisticated approach.
  2. Test “causation” by evaluating super exogeneity. In many economic problems especially, the possibility of a randomised control trial is unfeasible. Super exogeneity is an added layer of sophistication on the correlation/causation spectrum of which Granger causation was an early addition.
  3. Deal with hidden dependence in cross-section data. Not always an easy prospect to manage, cross-sectional dependence usually has no natural or obvious ordering as in time series dependence: but controlling for this is critical.
  4. Correct for selection biases. Often, big data arrives not out of a careful sampling design, but on a “whoever turned up to the website” basis. Recognising, controlling and correcting for this is critical to good model selection.

Doornik and Hendry advocate the use of autometrics in the presence of big data, without abandoning statistical rigour. Failing to understand the statistical consequences of our modelling techniques makes poor use of data assets that otherwise have immense value. Doornik and Hendry propose a robust and achievable methodology. Go read their paper!

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