Monday, March 24, 2014

Thumbs Up; Thumbs Down

People say and do the darnedest things! 

I'll let you assign your own "thumbs up" and "thumbs down" to the following gems. I imagine you can guess where I stand on each of them!

'But which is a bigger menace to society, laziness about data or laziness about theory? Theory-laziness is seductive because it's easy - mining for correlations isn't very mentally taxing. But data-laziness is seductive because it's hard - the more complicated and intricate a theory you make, the smarter it makes you feel, even if the theory sucks. 
 In the past, data-laziness was probably more of a threat to humanity. Since systematic data was scarce, people had a tendency to sit around and daydream about how stuff might work. But now that Big Data is getting bigger and computing power is cheap, theory-laziness seems to be becoming more of a menace. The lure of Big Data is that we can get all our ideas from mining for patterns, but A) we get a lot of false patterns that way, and B) the patterns insidiously and subtly suggest interpretations for themselves, and those interpretations are often wrong.'
(Noah Smith in his post, Which is Better, Data or Theory?)

'........ which raises the question "who should be teaching students econometrics?" Should it be someone like ****, who is basically an applied micro guy, or should it be an econometric theorist?' 
(Frances Woolley, commenting on her own post)

'Developing statistical methods is hard and often frustrating work. One of the under appreciated rules in statistical methods development is what I call the 80/20 rule (maybe could even by the 90/10 rule). The basic idea is that the first reasonable thing you can do to a set of data often is 80% of the way to the optimal solution. Everything after that is working on getting the last 20%.'
(Jeff Leek, on the Simply Statistics blog)

'The micro stuff that people like myself and most of us do has contributed tremendously and continues to contribute. Our thoughts have had enormous influence. It just happens that macroeconomics, firstly, has been done terribly and, secondly, in terms of academic macroeconomics, these guys are absolutely useless, most of them. Ask your brother-in-law. I’m sure he thinks, as do 90% of us, that most of what the macro guys do in academia is just worthless rubbish. Worthless, useless, uninteresting rubbish, catering to a very few people in their own little cliques.'
(Chris Auld, reputedly quoting someone else, in a blog post from 2011)

'The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data.'
(John Tukey)
'So, we produce our papers, as if on a relentless production line. We cannot wait for inspiration; we must maintain our output. To do our jobs successfully, we need to acquire a fundamental academic skill that the scholars of old generally did not possess; modern academics must be able to keep writing and publishing even when they have nothing to say. ....'
(Michael Billig, as quoted by Timothy Taylor)

So, thumbs up, and thumbs down. Or, from the sublime to the ridiculous - take your pick.
Boy - it was hard to resist giving my reaction  to some of these!
© 2014, David E. Giles


  1. Dave, resistance is futile. Tell me what you think!

    1. Frances - well, just for you!

      Your comment - what I think is that we need courses in applied econometrics AND courses in econometric theory. And everywhere I've taught, that's exactly what we've had at the grad. level - and except for here at UVic, that's what we've always had at the undergrad. level too.

      There's a really important place for both types of courses, and the applied courses should be taught by people who do applied work. It's hands-on stuff, and it's not easy to teach such courses. This deals with the issue of students "going back and forth" between the theory and the application that you refer to in your post.

      At Uvic. we "compromise" with most of our undergrad. econometrics courses. With one exception, they're essentially theory, but they do have pretty solid weekly (computer) lab. sessions, with exercises involving real data - both cross-section and time-series. It's far from perfect.

      At the grad level we have several econometric theory courses, and in addition we have an "applied econometrics" course, a very applied "time series econometrics" course, and a very applied "cross-section econometrics" course. Almost all students elect to take some mixture of applied and theory (econometrics) courses, and personally I think they get a good overall grounding in econometrics.

      Now, as for other quotes.......... I'll keep you guessing.

    2. And I should add - in the elective grad. econometric theory I am currently teaching, all of the 20 students have to undertake a major empirical project, singly or in pairs. This project goes on all semester, it's of their own design, and they have to make presentations at the end as well as hand in a written report. I wouldn't call this taking the easy way out.

  2. Dave, my class started doing their final presentations today. I asked them all, at the end of their final presentation, to say what they'd learned in the course. They mostly said "how to use Stata" but also people said things like "I learned how to read an economics paper, and interpret the regression results" and "I learned how broad the scope of microeconomics is." I almost cried, I was so touched! Course the fact that I was sitting on my computer grading them on their comments means the remarks should be taken with a grain or several of salt....

    1. Frances - that really IS a great outcome!

    2. My 546 "Themes in Econometrics" class is working on their last assignment. In addition to working on some Bayesian econometrics problems, they also have to discuss and critique a short paper published in "Economics Letters", in which GMM estimation is used. Each student has their own individual paper to discuss - I'll be very interested to see what they come up with.

  3. Correction: the quote Dave attributes to me is actually Dan Hamermesh.

    1. Some quotation marks in your blog post would have made that clear.

    2. So, do you agree with Dan, or not?


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