Tag Archives: Statistics & Econometrics

Statistical models for causation — a critical review Causal inferences can be drawn from nonexperimental data. However, no mechanical rules can be laid down for the activity. Since Hume, that is almost a truism. Instead, causal inference seems to require an enormous investment of skill, intelligence, and hard work. Many convergent lines of evidence must be developed. Natural variation needs to be identified and exploited. Data must be collected. Confounders...

Randomness and probability — a theoretical reexamination Modern probabilistic econometrics relies on the notion of probability. To at all be amenable to econometric analysis, economic observations allegedly have to be conceived as random events. But is it really necessary to model the economic system as a system where randomness can only be analyzed and understood when based on an a priori notion of probability? In probabilistic econometrics, events and...

Econometrics — a critical realist critique 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 really a gross misapprehension. To be careful and cautious is not the same as to dislike. And as any perusal of the mathematical-statistical and philosophical works of people like for example Nancy Cartwright, Chris Chatfield,...

The history of econometrics There have been over four decades of econometric research on business cycles … But the significance of the formalization becomes more difficult to identify when it is assessed from the applied perspective … The wide conviction of the superiority of the methods of the science has converted the econometric community largely to a group of fundamentalist guards of mathematical rigour … So much so that the relevance of the research to...

Econometrics — the scientific illusion of an empirical failure Ed Leamer’s Tantalus on the Road to Asymptopia is one of yours truly’s favourite critiques of econometrics, and for the benefit of those who are not versed in the econometric jargon, this handy summary gives the gist of it in plain English: Most work in econometrics and regression analysis is made on the assumption that the researcher has a theoretical model that is ‘true.’ Based on this belief...

Why I am not a Bayesian Assume you’re a Bayesian turkey and hold a nonzero probability belief in the hypothesis H that “people are nice vegetarians that do not eat turkeys and that every day I see the sun rise confirms my belief.” For every day you survive, you update your belief according to Bayes’ Rule P(H|e) = [P(e|H)P(H)]/P(e), where evidence e stands for “not being eaten” and P(e|H) = 1. Given that there do exist other hypotheses than H, P(e) is less...

Econometrics — a crooked path from cause to effect  [embedded content] In their book Mastering ‘Metrics: The Path from Cause to Effect Joshua Angrist and Jörn-Steffen Pischke write: Our first line of attack on the causality problem is a randomized experiment, often called a randomized trial. In a randomized trial, researchers change the causal variables of interest … for a group selected using something like a coin toss. By changing circumstances randomly,...

On randomization and regression (wonkish) Randomization does not justify the regression model, so that bias can be expected, and the usual formulas do not give the right variances. Moreover, regression need not improve precision … What is the source of the bias when regression models are applied to experimental data? In brief, the regression model assumes linear additive effects. Given the assignments, the response is taken to be a linear combina- tion of...

Workshop on causal graphs (student stuff)  [embedded content]

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...