Tag Archives: Statistics & Econometrics

The dangers of calling your pet cat a dog 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...

The alleged success of econometrics Econometricians typically hail the evolution of econometrics as a “big success”. For example, Geweke et al. (2006) argue that “econometrics has come a long way over a relatively short period” … Pagan (1987) describes econometrics as “outstanding success” because the work of econometric theorists has become “part of the process of economic investigation and the training of economists” … These claims represent no more than...

P-value — a poor substitute for scientific reasoning [embedded content]All science entails human judgment, and using statistical models doesn’t relieve us of that necessity. Working with misspecified models, the scientific value of significance testing is actually zero — even though you’re making valid statistical inferences! Statistical models and concomitant significance tests are no substitutes for doing real science. In its standard form, a significance...

Golden ratio (student stuff)  [embedded content]

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...