Mittwoch, 13. August 2014

Data Mining

... not as bad as you may have thought.

Jennifer Castle and David Hendry aus Oxford, der aktuellen Hochburg in Bezug auf Modellselektionsverfahren, schreiben auf vox über data-mining-Verfahren für große Datensätze (N>T).
"Econometric models need to handle many complexities if they are to have any hope of approximating the real world. There are many potentially relevant variables, dynamics, outliers, shifts, and non-linearities that characterise the data generating process. All of these must be modelled jointly to build a coherent empirical economic model, necessitating some form of data mining – see the approach described in Castle et al. (2011) and extensively analysed in Hendry and Doornik (2014).

Any omitted substantive feature will result in erroneous conclusions, as other aspects of the model attempt to proxy the missing information. At first sight, allowing for all these aspects jointly seems intractable, especially with more candidate variables (denoted N) than observations (T denotes the sample size). But help is at hand with the power of a computer. [...]"
Ein interessanter Beitrag zu einem Thema, das im Zuge der Verbeitung großer Datensätze in Zukunft noch viel stärker in den Fokus von Ökonomen und Ökonometrikern rutschen wird.

David Hendry und seine Truppe haben in den vergangenen Jahren im Bereich der general-to-specific-Modellierung (GETS) dafür schon sehr, sehr gute Grundlagen geschaffen.

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