Tag Archives: Statistics & Econometrics

Science — a messy business The obvious response of course, albeit one that econometricians occupied with fitting a line to given sets of data rarely contemplate, is to add to the ‘available data.’ Specifically the aim must be to draw consequences for, and seek out observations on, actual phenomena which allow the causal factor responsible to be identified. If, for example, bird droppings is a relevant causal factor then we could expect higher yields...

With models, it is easy to lose track of three essential points: (i) results depend on assumptions, (ii) changing the assumptions in apparently innocuous ways can lead to drastic changes in conclusions, and (iii) familiarity with a model’s name is no guarantee of the model’s truth. Under the circumstances, it may be the assumptions behind the model that provide the leverage, not the data fed into the model. This is a danger with experiments, and even more so with...

How to achieve exchangeability (student stuff) .[embedded content]

.[embedded content] Great presentation, but I do think Angrist ought to have also mentioned that although ‘ideally controlled experiments’ may tell us with certainty what causes what effects, this is so only when given the right ‘closures.’ Making appropriate extrapolations from — ideal, accidental, natural or quasi — experiments to different settings, populations or target systems, is not easy. “It works there” is no evidence for “it will work here.” The causal background...

Econometric toolbox developers get this year’s ‘Nobel prize’ in economics Many of the big questions in the social sciences deal with cause and effect. How does immigration affect pay and employment levels? How does a longer education affect someone’s future income? … This year’s Laureates have shown that it is possible to answer these and similar questions using natural experiments. The key is to use situations in which chance events or policy changes...

Statistics and econometrics — science building on fantasy worlds In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes casual knowledge. This is like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come into the picture. The assumption of imaginary ‘super populations’ is one of the many...

Why technical fixes will not rescue econometrics On the issue of the various shortcomings of regression analysis and econometrics, no one sums it up better than David Freedman in his Statistical Models and Causal Inference: In my view, regression models are not a particularly good way of doing empirical work in the social sciences today, because the technique depends on knowledge that we do not have. Investigators who use the technique are not paying...

Science before statistics — causal inference .[embedded content]

Probability and rationality — trickier than most people think The Coin-tossing Problem My friend Ben says that on the first day he got the following sequence of Heads and Tails when tossing a coin: H H H H H H H H H H And on the second day he says that he got the following sequence: H T T H H T T H T H Which report makes you suspicious? Most people yours truly asks this question says the first report looks suspicious. But actually both reports are equally...

Reverse causal reasoning and inference to the best explanation One of the few statisticians that yours truly has on his blogroll is Andrew Gelman. Although not sharing his Bayesian leanings, I find  his thought-provoking and non-dogmatic statistical thinking highly recommendable. The plaidoyer infra for “reverse causal questioning” is typical Gelmanian: When statistical and econometrc methodologists write about causal inference, they generally focus on...