Tag Archives: Statistics & Econometrics

Feynman’s integral trick (student stuff) .[embedded content] I had learned to do integrals by various methods shown in a book that my high school physics teacher Mr. Bader had given me. [It] showed how to differentiate parameters under the integral sign – it’s a certain operation. It turns out that’s not taught very much in the universities; they don’t emphasize it. But I caught on how to use that method, and I used that one damn tool again and again. [If]...

You see it all the time in studies. “We controlled for…” And then the list starts … The more things you can control for, the stronger your study is — or, at least, the stronger your study seems. Controls give the feeling of specificity, of precision. But sometimes, you can control for too much. Sometimes you end up controlling for the thing you’re trying to measure … An example is research around the gender wage gap, which tries to control for so many things that it ends up...

Getting causality into statistics Because statistical analyses need a causal skeleton to connect to the world, causality is not extra-statistical but instead is a logical antecedent of real-world inferences. Claims of random or “ignorable” or “unbiased” sampling or allocation are justified by causal actions to block (“control”) unwanted causal effects on the sample patterns. Without such actions of causal blocking, independence can only be treated as a...

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...

The Keynes-Tinbergen debate on econometrics It is widely recognized but often tacitly neglected that all statistical approaches have intrinsic limitations that affect the degree to which they are applicable to particular contexts … John Maynard Keynes was perhaps the first to provide a concise and comprehensive summation of the key issues in his critique of Jan Tinbergen’s book Statistical Testing of Business Cycle Theories … Keynes’s intervention has, of...

Ed Leamer’s Tantalus on the Road to Asymptopia is one of my favourite critiques of econometrics, and for the benefit of those who are not versed in the econometric jargon, this handy summary gives the gist of it in plain English: Most work in econometrics is — still — made on the assumption that the researcher has a theoretical model that is ‘true.’ Based on this belief of having a correct specification for an econometric model or running a regression, one proceeds as if the...

Non-manipulability and the limits of potential outcome models The Potential Outcome framework starts by defining the potential outcomes with reference to a manipulation. In doing so it makes a distinction between attributes or pre-treatment variables which are fixed for the units in the population, and causes, which are potentially manipulable. This is related to the connection between causal statements and randomized experiments. The causes are...

Econometricians would like to project the image of agricultural experimenters who divide a farm into a set of smaller plots of land and who select randomly the level of fertilizer to be used on each plot. If some plots are assigned a certain amount of fertilizer while others are assigned none, then the difference between the mean yield of the fertilized plots and the mean yield of the unfertilized plots is a measure of the effect of fertilizer on agricultural yields. The...

Interpreting confounder and modifier coefficients The problem with a table presenting multiple estimated effect measures from the same model (“Table 2”) is that it encourages the reader to interpret all these estimates in the same way, typically as total-effect estimates. As illustrated above, the interpretation of a confounder effect estimate may be different than for the exposure effect estimate. Of course, it is possible that some secondary reported...

On gender and alcohol Breaking news! Using advanced multiple nonlinear regression models similar to those in recent news stories on alcohol and dairy and more than 3.6M observations from 1997 through 2012, I have found that drinking more causes people to turn into men! Across people drinking 0-7 drinks per day, each drink per day causes the drinker’s probability of being a man to increase by 10.02 percentage points (z=302.2, p<0.0001). Need I say,...