Tag Archives: Statistics & Econometrics

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...

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If contributions made by statisticians to the understanding of causation are to be taken over with advantage in any specific field of inquiry, then what is crucial is that the right relationship should exist between statistical and subject-matter concerns … Where the ultimate aim of research is not prediction per se but rather causal explanation, an idea of causation that is expressed in terms of predictive power — as, for example, ‘Granger’ causation — is likely to be found...

Economic growth and the size of the ‘private sector’ Economic growth has since long interested economists. Not least, the question of which factors are behind high growth rates has been in focus. The factors usually pointed at are mainly economic, social and political variables. In an interesting study from the University of  Helsinki, Tatu Westling has expanded the potential causal variables to also include biological and sexual variables. In  the report...

Milton Friedman’s critique of econometrics Tinbergen’s results cannot be judged by ordinary tests of statistical significance. The reason is that the variables with which he winds up, the particular series measuring these variables, the leads and lags, and various other aspects of the equations besides the particular values of the parameters (which alone can be tested by the usual statistical technique) have been selected after an extensive process of trial...

Econometrics textbooks — vague and confused causal analysis Econometric textbooks fall on all sides of this debate. Some explicitly ascribe causal meaning to the structural equation while others insist that it is nothing more than a compact representation of the joint probability distribution. Many fall somewhere in the middle – attempting to provide the econometric model with sufficient power to answer economic problems but hesitant to anger traditional...

Proper use of regression analysis Level I regression analysis does not require any assumptions about how the data were generated. If one wants more from the data analysis, assumptions are required. For a Level II regression analysis, the added feature is statistical inference: estimation, hypothesis tests and confidence intervals. When the data are produced by probability sampling from a well-defined population, estimation, hypothesis tests and confidence...