Tag Archives: Statistics & Econometrics

Probability vs Maximum Likelihood (student stuff) [embedded content] .[embedded content] Advertisements

On manipulability and causation If contributions made by statisticians to the understanding of causation are to be taken over with advantage in any specific field of inquiry, then what is crucial is that the right relationship should exist between statistical and subject-matter concerns …The idea of causation as consequential manipulation is apt to research that can be undertaken primarily through experimental methods and, especially to ‘practical science’...

Why most published research is wrong [embedded content] After having mastered all the technicalities of regression analysis and econometrics, students often feel as though they are the masters of the universe. I usually cool them down with a required reading of Christopher Achen‘s modern classic Interpreting and Using Regression. It usually get them back on track again, and they understand that no increase in methodological sophistication … alter the...

P-values are no substitute for thinking  [embedded content] A non-trivial part of statistics education is made up of teaching students to perform significance testing. A problem I have noticed repeatedly over the years, however, is that no matter how careful you try to be in explicating what the probabilities generated by these statistical tests really are, still most students misinterpret them. This is not to blame on students’ ignorance, but rather on...

In search of causality One of the few statisticians that yours truly have on the blogroll is Andrew Gelman. Although not sharing his Bayesian leanings, I find his open-minded, thought-provoking and non-dogmatic statistical thinking highly recommendable. The plaidoyer below for ‘reverse causal questioning’ is typical Gelmanian: When statistical and econometrc methodologists write about causal inference, they generally focus on forward causal questions. We...

Kausala modeller och heterogenitet (wonkish) I The Book of Why för Judea Pearl fram flera tunga skäl till varför den numera så populära kausala grafteoretiska ansatsen är att föredra framför mer traditionella regressionsbaserade förklaringsmodeller. Ett av skälen är att kausala grafer är icke-parametriska och därför inte behöver anta exempelvis additivitet och/eller frånvaro av interaktionseffekter — pilar och noder ersätter regressionsanalysens nödvändiga...

Good thinking — the thing statistics cannot​ replace  [embedded content] As social researchers, we should never equate science with mathematics and statistical calculation. All science entail human judgement, and using mathematical and statistical models don’t relieve us of that necessity. They are no substitutes for thinking and doing real science. Statistical — and econometric — patterns should never be seen as anything else than possible clues to follow....

Mainstream economists often hold the view that Keynes’ criticism of econometrics was the result of a sadly misinformed and misguided person who disliked and did not understand much of it. This is, however, nothing but a gross misapprehension. To be careful and cautious is not the same as to dislike. Keynes did not misunderstand the crucial issues at stake in the development of econometrics. Quite the contrary. He knew them all too well — and was not satisfied with the validity...

 [embedded content] If anything, Snow’s path-breaking research underlines how important it is not to equate science with statistical calculation. All science entail human judgement, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of statistics is actually zero — even though you’re making valid statistical inferences! Statistical models are no substitutes for doing real science. Or as a famous German...

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