Tag Archives: Statistics & Econometrics

Statistical models and the assumptions on which they build Every method of statistical inference depends on a complex web of assumptions about how data were collected and analyzed, and how the analysis results were selected for presentation. The full set of assumptions is embodied in a statistical model that underpins the method … Many problems arise however because this statistical model often incorporates unrealistic or at best unjustified assumptions …...

Support Vector Machines (student stuff) .[embedded content]

We are calling for a stop to the use of P values in the conventional, dichotomous way — to decide whether a result refutes or supports a scientific hypothesis … The rigid focus on statistical significance encourages researchers to choose data and methods that yield statistical significance for some desired (or simply publishable) result, or that yield statistical non-significance for an undesired result, such as potential side effects of drugs — thereby invalidating...

How to teach econometrics One way to change the sad state of econometrics — an industry with a huge output but no sales — would perhaps be to follow Ed Leamer’s advice on how to teach it … To take my students on a journey toward real intelligence, I have given them the following assignment: Your boss walks into your office and says, “I read in the newspaper this morning that interest rates are going to increase this year. Is this right, and do I need to...

Causal inference — what the machine cannot learn .[embedded content] The central problem with the present ‘machine learning’ and ‘big data’ hype is that so many — falsely — think that they can get away with analyzing real-world phenomena without any (commitment to) theory. But — data never speaks for itself. Without a prior statistical set-up, there actually are no data at all to process. Clever data-mining tricks are not enough to answer important...

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. It is often the case that mathemical rigour is held as the dominant goal and the criterion for research topic choice as well as research evaluation, so much so that the relevance of the research to business cycles is reduced to empirical illustrations. To that extent, probabilistic formalization has...

If you can’t devise an experiment that answers your question in a world where anything goes, then the odds of generating useful results with a modest budget and nonexperimental survey data seem pretty slim. The description of an ideal experiment also helps you formulate causal questions precisely. The mechanics of an ideal experiment highlight the forces you’d like to manipulate and the factors you’d like to hold constant. Research questions that cannot be answered by any...

Donald Rubin on the history of causal inference .[embedded content]

Propensity score analysis — some critical remarks Our findings suggest that researchers need comprehensive knowledge of model assumptions and knowledge of plausible causal structure. From prior research, sources of selection bias must be understood. Substantive knowledge of plausible causal structure typically includes the theory of change of an intervention program being evaluated, which determines the covariates that should be used in the model predicting...

History of econometric causality .[embedded content]