In one of my last posts I was not sure how R’s different ADF test functions worked in detail. So, based on this discussion thread I set up a simple test. I created four time series:
I just stumbled over a very nice article authored by Paul Teetor on the use of total least-squares-regression in contrast to ordinary-least-squares regression for cointegration tests. This blog post also explains the same topic.
In an earlier post I explained how to install Jekyll-Auth. In GitHub, every team (and organization and user) receives a six to seven digits integer number as an ID like 1234567. There are cases where you might need access to this information, for instance during the installation of Jekyll-Auth. Unfortunately, there is no easy way to find out a team’s ID. I could not find it anywhere published at the official GitHub website. You can however access this information through the GitHub API.
One of the most poorly understood and yet at the same time most important concepts of genetic programming (GP) is parsimony pressure. It has long ago been demonstrated that for every type of statistical time series a function can be invented that arbitrarily well matches the observed values in the given time frame if that function is just complex enough. Yet, such a function is effectively worthless. As soon as new observations are added or as soon as predictions should be made for values lying outside the observed time frame the function terribly fails to deliver any meaningful result. I am of course talking about the problem of overfitting.
Und erneut schreiben sie wieder - die Verschwörungstheoretiker. Diesmal im Falle Charlie Hebdos. Gar nicht tot, sei er, der erschossene Polizist. Als “Beweis” wird irgendein obskurer Videomitschnitt gezeigt. Es gibt viele Gründe, sich das nicht näher anzuschauen. Eine Reaktion - aus Empörung, man darf es sagen - hier trotzdem.
