Tag Archives: Statistics & Econometrics

The rhetoric of imaginary populations The most expedient population and data generation model to adopt is one in which the population is regarded as a realization of an infinite super population. This setup is the standard perspective in mathematical statistics, in which random variables are assumed to exist with fixed moments for an uncountable and unspecified universe of events … This perspective is tantamount to assuming a population machine that spawns...

One may wonder how much calibration adds to the knowledge of economic structures and the deep parameters involved … First, few ‘deep parameters’ have been established at all … Second, even where estimates are available from micro-econometric investigations, they cannot be automatically imported into aggregated general equilibrium models … Third, calibration hardly contributes to growth of knowledge about ‘deep parameters’. These deep parameters are confronted with a novel...

 [embedded content] A great video, but — there’s always a but — unfortunately also not without some analytical shortcomings. The point of making a randomized experiment is often said to be that it ‘ensures’ that any correlation between a supposed cause and effect indicates a causal relation. This is believed to hold since randomization (allegedly) ensures that a supposed causal variable does not correlate with other variables that may influence the effect. The problem with...

Haavelmo and modern probabilistic econometrics — a critical-realist perspective (wonkish) Mainstream economists often hold the view that criticisms of econometrics are the conclusions of sadly misinformed and misguided people who dislike and do not understand much of it. This is a gross misapprehension. To be careful and cautious is not equivalent to dislike. The ordinary deductivist ‘textbook approach’ to econometrics views the modelling process as...

Causal inference (student stuff)  [embedded content]

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...