Tag Archives: Statistics & Econometrics

Regression to the mean Regression to the men is nothing but the universal truth of the fact that whenever we have an imperfect correlation between two scores, we have regression to the mean. [embedded content]

How to do econometrics properly Always, but always, plot your data. Remember that data quality is at least as important as data quantity. Always ask yourself, “Do these results make economic/common sense”? Check whether your “statistically significant” results are also “numerically/economically significant”. Be sure that you know exactly what assumptions are used/needed to obtain the results relating to the properties of any estimator or test that you...

Keynes’ critique of econometrics — as valid today as it was in 1939 Renowned ‘error-statistician’ Aris Spanos maintains — in a comment on this blog a couple of weeks ago — that Keynes’ critique of econometrics and the reliability of inferences made when it is applied, “have been addressed or answered.” One could, of course, say that, but the valuation of the statement hinges completely on what we mean by a question or critique being ‘addressed’ or...

On the non-validity of incremental validity A common goal of statistical analysis in the social sciences is to draw inferences about the relative contributions of different variables to some outcome variable. When regressing academic performance, political affiliation, or vocabulary growth on other variables, researchers often wish to determine which variables matter to the prediction and which do not—typically by considering whether each variable’s...

Lindeberg-Levy CLT (student stuff)  [embedded content]

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...

Statistical inference — a self-imposed limitation The tool of statistical inference becomes available as the result of a self-imposed limitation of the universe of discourse. It is assumed that the available observations have been generated by a probability law or stochastic process about which some incomplete knowledge is available a priori … It should be kept in mind that the sharpness and power of these remarkable tools of inductive reasoning are bought...

Truth — not unbiasedness — is what we should aim for Econometricians usually aim for unbiased estimates. And in econometrics textbooks you learn that if it’s not BLUE, it’s not good. But if you really think about it, there is no real unbiased estimates. As soon as you weigh in the fact that in all econometric applications you always get your ‘unbiased’ estimates based on non-ideal randomized samples, measurement errors, non-additive and non-linear...

Post-model-selection inference problems (wonkish) It has long been recognized by some that when any parameter estimates are discarded, the sampling distribution of the remaining parameter estimates can be distorted … For example, suppose the model a researcher selects depends on the day of the week. On Mondays it’s model A, on Tuesdays it’s model B, and so onup to seven different models on seven different days. Each model, therefore,is the “final” model with...

The problem of nonexcitation (wonkish) Modern econometrics is fundamentally based on assuming — usually without any explicit justification — that we can gain causal knowledge by considering independent variables that may have an impact on the variation of a dependent variable. This is however, far from self-evident. Often the fundamental causes are constant forces that are not amenable to the kind of analysis econometrics supplies us with. As Stanley...