Tag Archives: Statistics & Econometrics

Shortcomings of regression analysis Distinguished social psychologist Richard E. Nisbett has a somewhat atypical aversion to multiple regression analysis. In his Intelligence and How to Get It (Norton 2011) he writes: Researchers often determine the individual’s contemporary IQ or IQ earlier in life, socioeconomic status of the family of origin, living circumstances when the individual was a child, number of siblings, whether the family had a library card,...

Keynes’ critique of econometrics — still valid after all these years To apply statistical and mathematical methods to the real-world economy, the econometrician has to make some quite strong assumptions. In a review of Tinbergen’s econometric work — published in The Economic Journal in 1939 — John Maynard Keynes gave a comprehensive critique of Tinbergen’s work, focusing on the limiting and unreal character of the assumptions that econometric analyses build...

How to interpret and use regression analysis After having mastered all the technicalities of regression analysis and econometrics, students often feel as though they are the masters of the universe. I usually cool them down with a required reading of Christopher Achen’s modern classic — Interpreting and Using Regression. It usually gets them back on track again, and they understand that “no increase in methodological sophistication … alter the fundamental...

After the 1920s, the theoretical and methodological approach to economics deeply changed … A new generation of American and European economists developed Walras’ and Pareto’s mathematical economics. As a result of this trend, the Econometric Society was founded in 1930 … In the late 1930s, John Maynard Keynes and other economists objected to this recent “mathematizing” approach … At the core of Keynes’ concern laid the question of methodology. Maria Alejandra Madi Keynes’...

Non-ergodicity and the poverty of kitchen sink modeling  [embedded content] When I present this argument … one or more scholars say, “But shouldn’t I control for everything I can in my regressions? If not, aren’t my coefficients biased due to excluded variables?” This argument is not as persuasive as it may seem initially. First of all, if what you are doing is misspecified already, then adding or excluding other variables has no tendency to make things...

Paul Rosenbaum’s latest book — Observation and experiment: an introduction to causal inference — is a well-written introduction to some of the most important and far-reaching ideas in modern statistics. With only a minimum of mathematics, ​the author manages to give a lively and interesting​ account of how statisticians try to use statistics to make causal inferences from observational studies and experiments. For non-graduate social science students with no or little...

Because I was there when the economics department of my university got an IBM 360, I was very much caught up in the excitement of combining powerful computers with economic research. Unfortunately, I lost interest in econometrics almost as soon as I understood how it was done. My thinking went through four stages: 1. Holy shit! Do you see what you can do with a computer’s help. 2. Learning computer modeling puts you in a small class where only other members of the caste can...

The limits of probabilistic reasoning Probabilistic reasoning in science — especially Bayesianism — reduces questions of rationality to questions of internal consistency (coherence) of beliefs, but, even granted this questionable reductionism, it’s not self-evident that rational agents really have to be probabilistically consistent. There is no strong warrant for believing so. Rather, there is strong evidence for us encountering huge problems if we let...

Hierarchical models​ and clustered residuals (student stuff)  [embedded content] Advertisements

Exaggerated and unjustified statistical​ claims  [embedded content] Advertisements