Tag Archives: Statistics & Econometrics

Partiell integration med DI-metoden (student stuff) .[embedded content]

I The Book of Why för Judea Pearl fram flera tunga skäl till varför den numera så populära kausala grafteoretiska ansatsen är att föredra framför mer traditionella regressionsbaserade förklaringsmodeller. Ett av skälen är att kausala grafer är icke-parametriska och därför inte behöver anta exempelvis additivitet och/eller frånvaro av interaktionseffekter — pilar och noder ersätter regressionsanalysens nödvändiga specificeringar av funktionella relationer mellan de i...

Logistic regression (student stuff) .[embedded content] In the video below (in Swedish) yours truly shows how to perform a logit regression using Gretl.[embedded content]

The leap of generalization Statistician Andrew Gelman has an interesting blogpost up on what inference in science really means: I like Don Rubin’s take on this, which is that if you want to go from association to causation, state very clearly what the assumptions are for this step to work. The clear statement of these assumptions can be helpful in moving forward … Another way to say this is that all inference is about generalizing from sample to population,...

Keynes on the methodology of econometrics There is first of all the central question of methodology — the logic of applying the method of multiple correlation to unanalysed economic material, which we know to be non-homogeneous through time. If we are dealing with the action of numerically measurable, independent forces, adequately analysed so that we were dealing with independent atomic factors and between them completely comprehensive, acting with...

On the difference between econometrics and data science .[embedded content] Causality in social sciences can never solely be a question of statistical inference. Causality entails more than predictability, and to really in depth explain social phenomena require theory. The analysis of variation can never in itself reveal how these variations are brought about. First when we are able to tie actions, processes or structures to the statistical relations...

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...