On microfoundations

Some recent post on the necessity of microfoundations that I found interesting.

  1. Issues about microfoundations.
  2. Why you don’t need rigorous microfoundations.
  3. UpdateZombie Marx – illustrating the similarities between Marxism and Neo-classical economics.  ht Guan.

I agree with these in part, especially when it comes to macroeconomics (whether this is appropriate depends on your view on the nature of how we should treat macroeconomic aggregates).  But as with all things it is a matter of balance.

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QOTD: Déformation professionnelle

Every specialist, owing to a well-known professional bias, believes that he understands the entire human being, while in reality he only grasps a tiny part of him.

Alexis Carrel

HT: Chris Dillow

It’s economic analysis, not commentary

Every time the statistical authority releases new data there is a surge in economic commentary. Not analysis, but commentary. A thoughtful analysis would usually say that a single new data point doesn’t provide enough information to change anything we thought previously. There’s just too much randomness and error in point estimates to be able to tell much from them. Commentary is different because it creates a narrative and fits the data into that narrative.

A good example is the narrative about double and triple-dips in the UK. Commentators made much of the ONS’ revisions to the GDP series that ‘revised away’ the triple-dip, ‘vindicating Osborne’. The revisions may have eliminated a slight dip in GDP but they didn’t change anyone’s understanding of what had happened in the macroeconomy. That data was important for commentary but not for analysis. In fairness to commentators, distinguishing genuine trends from randomness is not easy. Our eyes are drawn to ‘streaks’, whether in football games or economic time series, even when the series is essentially random. Economists are always looking for techniques to separate the streaks from the randomness. The problem we face is that many of the tools are fairly impenetrable to casual observers and hard to explain.

Edward Tufte has suggested using randomised sparklines to visually distinguish genuine trends from deceptive streaks, so I thought I’d give it a go with the last four years of UK unemployment data. Here is the monthly change in the UK unemployment rate since June 2009: Monthly percentage point change in UK unemployment rate: June 2009-October 2013. We think that a recovery has begun so the recent years’ falling unemployment looks good. Now let’s try randomising the values and see if the ‘streak’ disappears. Read more

Quote of the day: Pinker on changes in social sciences

Via Noah Smith.  We have the following post on the Pinker vs Wieseltier debate on science and humanities (if you have a chance I would suggest reading the debates themselves as well).

The era in which an essayist can get away with ex cathedra pronouncements on factual questions in social science is coming to an end.

Very good, and Pinker’s co-operative version of science with the humanities seems appropriate to me (where instead we are merely asking about how to deal with certain propositions and using the best tools available).  I think Pinker won this debate, I am unsure why Wieseltier felt it necessary to take such an extreme position though – I think he initially believed Pinker was trying to force through a view based on the superiority of scientific authority (one that Pinker rules out in his initial article!), when he was really just suggesting the use of the scientific method (namely introducing a degree of the positivist view of theory creation) given the improvements in data availability and usability we have had.

As XKCD says:

But even within Pinker’s reasonable claims there is one area where I would be a touch careful Read more

Where does the burden of proof lie?

Antonio Fatas discusses inherent bias in economics given our reference point – an important issue, and one that economists need to think on (Note:  James wrote on this as well – we did our posts separately, so are focusing on different points).  Specifically:

This subtle (or not so subtle) bias in economic analysis is my biggest source of frustration with my profession. Not being able to predict crisis, the stock market or exchange rates does not bother me, it is just a reflection of the limits of our knowledge and I can live with it. But using the same naive predictions of models that refer to a fictitious world as the reference and only moving away from them when someone produces an unquestionable piece of empirical evidence is in my mind the true cost of our profession to society.

His post raises good points, but I suspect that what he is laying out runs into the same problem many of us run into when using models to think about policy and the impact of policy – we are not being clear on where the “burden of proof” lies or what we think about assumptions.

Now I like his writing, and this is a good post, but I have a bit of a different view on what economists do with reference to this.  Perhaps New Zealand economists are a bit different?  Essentially, the question of burden of proof is usually treated as a central part of how we frame and discuss policy questions in New Zealand, so it becomes part of the way we discuss models.

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How do we deal with evidence? Badly.

We deal very badly with empirical falsification of our reference model:

Many of these assumptions are unrealistic but they are justified as a way to set a benchmark model around which one is then allowed to model deviations from the assumptions.

the problem is that the model becomes (or has become) the reference in a way that sets a high burden of proof for any deviations from it. If you think individuals are not rational, go ahead and model their behavior but you should do it in a way that is realistic and backed by data (good luck). Read more