Tag Archives: Statistics & Econometrics

Endogeneity bias — fiction in a fictitious world (wonkish) The bivariate model base and its a priori closure destines ‘endogeneity bias’ to a fictitious existence. That existence, in turn, confines applied research in a fictitious world. The concept loses its grip in empirical studies whose findings rely heavily on forecasting accuracy, e.g. a wide range of macro-modelling research as mentioned before. It remains thriving in areas where empirical results...

My favourite statistics books Mathematical statistician David Freedman‘s Statistical Models and Causal Inference (Cambridge University Press, 2010)  and Statistical Models: Theory and Practice (Cambridge University Press, 2009) are marvellous books. They ought to be mandatory reading for every serious social scientist — including economists and econometricians — who doesn’t want to succumb to ad hoc assumptions and unsupported statistical conclusions! How...

Rejecting positivism — the case of statistics Rejecting positivism requires re-thinking the disciplines related to data analysis from the foundations. In this paper, we consider just one of the foundational concepts of statistics. The question we will explore is: What is the relationship between the numbers we use (the data) and external reality? The standard conception promoted in statistics is that numbers are FACTS. These are objective measures of...

Gretl — econometrics made easy  [embedded content] Thanks to Allin Cottrell and Riccardo Lucchetti we today have access to a high-quality​ tool for doing and teaching econometrics — Gretl. And, best of all, it is totally free! Gretl is up to the tasks you may have, so why spend money on expensive commercial programs? The latest snapshot version of Gretl can be downloaded here. [And yes, I do know there’s another fabulously good and free program — R. But R...

A primer on causal inference  [embedded content] D H Kim’s twelve videos give a splendid introduction to modern thinking on causality. Highly recommendable student stuff!

Causation and causal inference [embedded content] Who said science presentations have to be boring?

Prediction vs Causal Inference  [embedded content]

Econometrics as a testing device 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.” I usually respond by referring to the text below … Most econometricians today … believe that the main objective of applied econometrics is the confrontation of economic theories with...

Econometrics — a matter of BELIEF and FAITH Everybody who takes regression analysis course, studies the assumptions of regression model. But nobody knows why, because after reading about the axioms, they are rarely mentioned. But the assumptions are important, because if any one assumption is wrong, the regression is not valid, and the interpretations can be completely wrong. In order to have a valid regression model, you must have right regressors, the...

Econometrics and the Axiom of Omniscience 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 to good to be true, they usually aren’t. And that...