Tag Archives: Statistics & Econometrics

How to teach econometrics Professor Swann (2019) seems implicitly to be endorsing the traditional theorem/proof style for teaching econometrics but with a few more theorems to be memorized. This style of teaching prepares students to join the monks in Asymptopia, a small pristine mountain village, where the monks read the tomes, worship the god of Consistency, and pray all day for the coming of the Revelation, when the estimates with an infinite sample will...

One of the most beautiful results of probability theory is Markov’s inequality (after the Russian mathematician Andrei Markov (1856-1922)): If X is a non-negative stochastic variable (X ≥ 0) with a finite expectation value E(X), then for every a > 0 P{X ≥ a} ≤ E(X)/a If the production of cars in a factory during a week is assumed to be a stochastic variable with an expectation value (mean) of 50 units, we can – based on nothing else but the inequality – conclude that the...

Econometrics — fictions of extremely limited value It is often said that the error term in a regression equation represents the effect of the variables that were omitted from the equation. This is unsatisfactory … There is no easy way out of the difficulty. The conventional interpretation for error terms needs to be reconsidered. At a minimum, something like this would need to be said: The error term represents the combined effect of the omitted variables,...

DAGs — colliders and d-separation (wonkish)  [embedded content] Great lecture on something that most students have problems with when introduced to causal graph theory.

Why the father of modern statistics — R A Fisher — denied smoking causes cancer In 1959, Fisher denounced his colleagues for manufacturing anti-smoking “propaganda” … He did not dispute that smoking and lung cancer tended to rise and fall together—that is, that they were correlated. But Hill and Doll and the entire British medical establishment had committed “an error … of an old kind, in arguing from correlation to causation,” he wrote in a letter to...

Collider attributes in graph theory (wonkish) Why would two independent variables suddenly become dependent when we condition on their common effect? To answer this question, we return again to the definition of conditioning as filtering by the value of the conditioning variable. When we condition on Z, we limit our comparisons to cases in which Z takes the same value. But remember that Z depends, for its value, on X and Y. So, when comparing cases where Z...

Why validating assumptions is so important in science An ongoing concern is that excessive focus on formal modeling and statistics can lead to neglect of practical issues and to overconfidence in formal results … Analysis interpretation depends on contextual judgments about how reality is to be mapped onto the model, and how the formal analysis results are to be mapped back into reality. But overconfidence in formal outputs is only to be expected when much...

Confusing statistics and research Coupled with downright incompetence in statistics, we often find the syndrome that I have come to call statisticism: the notion that computing is synonymous with doing research, the naïve faith that statistics is a complete or sufficient basis for scientific methodology, the superstition that statistical formulas exist for evaluating such things as the relative merits of different substantive theories or the “importance”...

The pitfalls of econometrics Ed Leamer’s Tantalus on the Road to Asymptopia is one of my favourite critiques of econometrics, and for the benefit of those who are not versed in the econometric jargon, this handy summary gives the gist of it in plain English: Most work in econometrics and regression analysis is made on the assumption that the researcher has a theoretical model that is ‘true.’ Based on this belief of having a correct specification for an...

Econometrics — the danger of calling your pet cat a dog Since econometrics doesn’t content itself with only making optimal predictions, but also aspires to explain things in terms of causes and effects, econometricians need loads of assumptions — most important of these are additivity and linearity. Important, simply because if they are not true, your model is invalid and descriptively incorrect.  And when the model is wrong — well, then it’s wrong. The...