Tag Archives: Statistics & Econometrics

Read my lips — using an RCT guarantees nothing! The claimed hierarchy of methods, with randomized assignment being deemed inherently superior to observational studies, does not survive close scrutiny. Despite frequent claims to the contrary, an RCT does not equate counterfactual outcomes between treated and control units. The fact that systematic bias in estimating the mean impact vanishes in expectation (under ideal conditions) does not imply that the...

‘Doctor, it hurts when I p’ A low-powered study is only going to be able to see a pretty big effect. But sometimes you know that the effect, if it exists, is small. In other words, a study that accurately measures the effect … is likely to be rejected as statistically insignificant, while any result that passes the p < .05 test is either a false positive or a true positive that massively overstates the … effect. … A conventional boundary, obeyed long...

An introduction to probability density functions (student stuff)  [embedded content]

Some basic COVID-19 mathematics  [embedded content]

Simpson’s paradox and the limits of econometrics  [embedded content] From a more theoretical perspective, Simpson’s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities. To understand causality we always have to relate it to a specific causal structure. Statistical correlations are never enough. No structure, no causality. Simpson’s paradox is an interesting paradox in itself, but it can also highlight a...

Economic growth and the size of the ‘private sector’ Economic growth has since long interested economists. Not least, the question of which factors are behind high growth rates has been in focus. The factors usually pointed at are mainly economic, social and political variables. In an interesting study from the University of  Helsinki, Tatu Westling expanded the potential causal variables to also include biological and sexual variables. In the report Male...

Econometric modelling as junk science Do you believe that 10 to 20% of the decline in crime in the 1990s was caused by an increase in abortions in the 1970s? Or that the murder rate would have increased by 250% since 1974 if the United States had not built so many new prisons? Did you believe predictions that the welfare reform of the 1990s would force 1,100,000 children into poverty? If you were misled by any of these studies, you may have fallen for a...

Econometrics — the signal-to-noise problem When we first encounter the term, “noisy data,” in econometrics, we are usually told that it refers to the problem of measurement error, or errors-in-variables—especially in the explanatory variables (x). Most textbooks contain a discussion of measurement error bias. In the case of a bivariate regression, y = a + bx + u, measurement error in x means the ordinary least squares (OLS) estimator is biased. The...

Debating econometrics and its short-comings yours truly often gets the response from econometricians that “ok, maybe econometrics isn’t perfect, but you have to admit that it is a great technique for empirical testing of economic hypotheses.” But is econometrics — really — such a great testing instrument? Econometrics is supposed to be able to test economic theories but to serve as a testing device you have to make many assumptions, many of which themselves cannot be tested or...

Why are election polls often inaccurate? Why is racism wrong? Why are your assumptions often mistaken? The answers to all these questions and to many others have a lot to do with the non-ergodicity of human ensembles. Many scientists agree that ergodicity is one of the most important concepts in statistics. So, what is it? Suppose you are concerned with determining what the most visited parks in a city are. One idea is to take a momentary snapshot: to see how many people are...