Strophyx meinte am 16. Jul, 07:07:
Advancement Bias
As one of my professors in graduate school noted, professional journals want to publish results that are statistically significant much more results that fail to detect any significant associations. Since academic and professional advancement is strongly tied to records of publications, there's a strong tendency for researchers to keep tweaking their models until they find something with the veneer of statistical significance, then creating a theory or hypothesis which purports to be supported by these results. In one of my first professional jobs a colleague and I wrote a program to take any dataset and run a huge number of different models against the data. (I think that 10 variables resulted in roughly 1,000,000 different models.) The results were then sorted by the standard tests of statistical significance, and the most "significant" models printed. We did it as a joke, only to be disgusted to find that many researchers at the same company wanted to get a copy to use.
Mahalanobis antwortete am 16. Jul, 11:56:
He who mines data may strike fool's gold
You have probably heard of David Leinweber who sifted through a United Nations CD-ROM and discovered that the single best predictor of the Standard & Poor's 500 stock index was butter production in Bangladesh... ;-D.A friend of mine once estimated a huge number of ARMA models of GDP growth rates for his diploma thesis... I told him that besides the fact that I wouldn't estimate models with MA terms with Eviews and besides the fact that nobody who has ever seen recursive parameter estimates of ARIMA models speaks positively of such models that he would somehow have to adjust the test size...
His answer: I do not need a good model, I need a dimploma thesis.
