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Tag Archives: Statistics & Econometrics

The fundamental flaw of econometrics

The fundamental flaw of econometrics 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, assuming that...

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Teflon-coated economics

At least since the time of Keynes’s famous critique of Tinbergen’s econometric methods, those of us in the social science community who have been unpolite enough to dare questioning the preferred methods and models applied in quantitive research in general and economics more specifically, are as a rule met with disapproval. Although people seem to get very agitated and upset by the critique — just read the commentaries on this blog if you don’t believe me — defenders of...

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Econometric forecasting — an assessment

Econometric forecasting — an assessment There have been over four decades of econometric research on business cycles … The formalization has undeniably improved the scientific strength of business cycle measures … But the significance of the formalization becomes more difficult to identify when it is assessed from the applied perspective, especially when the success rate in ex-ante forecasts of recessions is used as a key criterion. The fact that the onset...

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Racial bias in police shooting

Racial bias in police shooting Roland Fryer, an economics professor at Harvard University, recently published a working paper at NBER on the topic of racial bias in police use of force and police shootings. The paper gained substantial media attention – a write-up of it became the top viewed article on the New York Times website. The most notable part of the study was its finding that there was no evidence of racial bias in police shootings, which Fryer...

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What makes most econometric models invalid

What makes most econometric models invalid The assumption of additivity and linearity means that the outcome variable is, in reality, linearly related to any predictors … and that if you have several predictors then their combined effect is best described by adding their effects together … This assumption is the most important because if it is not true then even if all other assumptions are met, your model is invalid because you have described it...

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How do we attach probabilities to the real world?

How do we attach probabilities to the real world? Econometricians usually think that the data generating process (DGP) always can be modelled properly using a probability measure. The argument is standardly based on the assumption that the right sampling procedure ensures there will always be an appropriate probability measure. But – as always – one really has to argue the case, and present warranted evidence that real-world features are correctly described...

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Is ‘Cauchy logic’ applicable to economics?

Is ‘Cauchy logic’ applicable to economics? What is 0.999 …, really? It appears to refer to a kind of sum: .9 + + 0.09 + 0.009 + 0.0009 + … But what does that mean? That pesky ellipsis is the real problem. There can be no controversy about what it means to add up two, or three, or a hundred numbers. But infinitely many? That’s a different story. In the real world, you can never have infinitely many heaps. What’s the numerical value of an infinite sum? It...

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Invariance assumptions and econometric ‘causality’

In order to make causal inferences from simple regression, it is now conventional to assume something like the setting in equation (1) … The equation makes very strong invariance assumptions, which cannot be tested from data on X and Y. (1) Y = a + bx + δ What happens without invariance? The answer will be obvious. If intervention changes the intercept a, the slope b, or the mean of the error distribution, the impact of the intervention becomes difficult to determine. If the variance of...

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Bayesian overload

Although Bayesians think otherwise, to me there’s nothing magical about Bayes’ theorem. The important thing in science is for you to have strong evidence. If your evidence is strong, then applying Bayesian probability calculus is rather unproblematic. Otherwise — garbage in, garbage out. Applying Bayesian probability calculus to subjective beliefs founded on weak evidence is not a recipe for scientific akribi and progress. Neoclassical economics nowadays usually assumes that agents that...

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