Wednesday , June 19 2024
Home / Tag Archives: Statistics & Econometrics (page 30)

Tag Archives: Statistics & Econometrics

Simpson’s paradox and the limits of econometrics

Simpson’s paradox and the limits of econometrics  [embedded content] From a more theoretical perspective, Simpson’s paradox importantly shows that causality can never be reduced to a question of statistics or probabilities. To understand causality we always have to relate it to a specific causal structure. Statistical correlations are never enough. No structure, no causality. Simpson’s paradox is an interesting paradox in itself, but it can also highlight a...

Read More »

Economic growth and the size of the ‘private sector’

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 expanded the potential causal variables to also include biological and sexual variables. In the report Male...

Read More »

Econometric modelling as junk science

Econometric modelling as junk science Do you believe that 10 to 20% of the decline in crime in the 1990s was caused by an increase in abortions in the 1970s? Or that the murder rate would have increased by 250% since 1974 if the United States had not built so many new prisons? Did you believe predictions that the welfare reform of the 1990s would force 1,100,000 children into poverty? If you were misled by any of these studies, you may have fallen for a...

Read More »

Econometrics — the signal-to-noise problem

Econometrics — the signal-to-noise problem When we first encounter the term, “noisy data,” in econometrics, we are usually told that it refers to the problem of measurement error, or errors-in-variables—especially in the explanatory variables (x). Most textbooks contain a discussion of measurement error bias. In the case of a bivariate regression, y = a + bx + u, measurement error in x means the ordinary least squares (OLS) estimator is biased. The...

Read More »

Econometric testing

Debating econometrics and its short-comings yours truly often gets the response from econometricians that “ok, maybe econometrics isn’t perfect, but you have to admit that it is a great technique for empirical testing of economic hypotheses.” But is econometrics — really — such a great testing instrument? Econometrics is supposed to be able to test economic theories but to serve as a testing device you have to make many assumptions, many of which themselves cannot be tested or...

Read More »

Ergodicity: a primer

Why are election polls often inaccurate? Why is racism wrong? Why are your assumptions often mistaken? The answers to all these questions and to many others have a lot to do with the non-ergodicity of human ensembles. Many scientists agree that ergodicity is one of the most important concepts in statistics. So, what is it? Suppose you are concerned with determining what the most visited parks in a city are. One idea is to take a momentary snapshot: to see how many people are...

Read More »

Machine learning — puzzling ‘big data’ nonsense

Machine learning — puzzling ‘big data’ nonsense If we wanted highly probable claims, scientists would stick to​​ low-level observables and not seek generalizations, much less theories with high explanatory content. In this day​ of fascination with Big data’s ability to predict​ what book I’ll buy next, a healthy Popperian reminder is due: humans also want to understand and to explain. We want bold ‘improbable’ theories. I’m a little puzzled when I hear...

Read More »

Statistical models for causation — a critical review

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...

Read More »