Tag Archives: Statistics & Econometrics

Statistical methods and empirical research David A. Freedman‘s Statistical Models: Theory and Practice (2009) is a marvellous book. It should be mandatory reading for every serious social scientist — including economists and econometricians — who don’t want to succumb to ad hoc assumptions and unsupported statistical conclusions! In the social and behavioral sciences, far-reaching claims are often made for the superiority of advanced quantitative methods —...

We who like to imagine ourselves responsible for the public’s knowledge of society despise description and indeed despise the methods that are generally used for quantitative description. Our social indicators are simply disaggre­gated variables, ready for input to causal analysis. The notions of complex combinatoric description, of typologies based on multiple variables — these fill the average sociologist with disgust. Our disgust is disingenuous, for ease of computing has...

Randomization-based vs. model-based inference .[embedded content]

The 20 Best Econometrics Blogs and Websites Yours truly, of course, feels truly honoured to find himself on the list of the world’s 20 Best Econometrics Blogs and Websites. 2. Bruno Rodrigues 7. Eran Raviv Blog Statistics and Econometrics 9. How the (Econometric) Sausage is Made 14. Lars P Syll Pålsson Syll received a PhD in economic history in 1991 and a PhD in economics in 1997, both at Lund University. He became an associate professor in economic history...

Causal inference — Fisher vs. Neyman (student stuff) .[embedded content]

Statistical assumptions as empirical commitments Real probability samples have two great benefits: (i) they allow unbiased extrapolation from the sample; (ii) with data internal to the sample, it is possible to estimate how much results are likely to change if another sample is taken. These benefits, of course, have a price: drawing probability samples is hard work. An investigator who assumes that a convenience sample is like a random sample seeks to...

Statistical reification — a deadly sin It would not be an exaggeration to say that many of us see academic statistics as “the sick man of science,” an alarming diagnosis in light of the central role it plays in science in general. The situation is maintained through specious and often byzantine rationales for destructive traditions, which is as expected from another human condition: Blindness to conceptual mistakes when correcting those would threaten the...

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How to avoid pernicious fictional statistics Mathematics is a limited component of solutions to real-world problems, as it expresses only what is expected to be true if all our assumptions are correct, including implicit assumptions that are omnipresent and often incorrect. Statistical methods are rife with implicit assumptions whose violation can be life-threatening when results from them are used to set policy. Among them are that there is human...

Econometrics — a mixture of ‘metaphors, metaphysics, and a pinch of bluff’ This effort to create foundations for the probability approach in econometrics finally results in an inconsistent set of claims in its defence. First, there are vast amounts of experience which warrant a frequency interpretation. This is supported by repetitive discussions of experimental design, but the inability to experiment inspires an epistemological interpretation. Then...