Tag Archives: Statistics & Econometrics

A non-trivial part of teaching statistics to social science students is made up of teaching them to perform significance testing. A problem yours truly has noticed repeatedly over the years, however, is that no matter how careful you try to be in explicating what the probabilities generated by these statistical tests — p-values — really are, still most students misinterpret them. A couple of years ago I gave a statistics course for the Swedish National Research School in...

All of these examples exhibit the confusion that often accompanies the drawing of causal conclusions from observational data. The likelihood of such confusion is not diminished by increasing the amount of data, although the publicity given to ‘big data’ would have us believe so. Obviously the flawed causal connection between drowning and eating ice cream does not diminish if we increase the number of cases from a few dozen to a few million. The amateur carpenter’s complaint...

Econometrics and the challenge of regression specification Most work in econometrics and regression analysis is — still — 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 econometric model or running a regression, one proceeds as if the only problem remaining to solve have to do with measurement and observation. When things sound too good to be true, they usually...

How scientists manipulate research [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 test is not the...

Econometrics — the art of pulling a rabbit out of a hat In econometrics one often gets the feeling that many of its practitioners think of it as a kind of automatic inferential machine: input data and out comes causal knowledge. This is — as Joan Robinson once had it — like pulling a rabbit from a hat. Great — but first you have to put the rabbit in the hat. And this is where assumptions come in to the picture. The assumption of imaginary ‘superpopulations’...

Fuzzy RDD & IV (student stuff) .[embedded content]

COVID19 and causal inference (wonkish) .[embedded content]

Natural experiments in the social sciences How, then, can social scientists best make inferences about causal effects? One option is true experimentation … Random assignment ensures that any differences in outcomes between the groups are due either to chance error or to the causal effect … If the experiment were to be repeated over and over, the groups would not differ, on average, in the values of potential confounders. Thus, the average of the average...

Causality and analysis of variation Modern econometrics is fundamentally based on assuming — usually without any explicit justification — that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable. This is however, far from self-evident. Often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with. As Stanley Lieberson...

Covariance algebra (student stuff) .                                                                                                                                                                                                 [embedded content]