That was a mistake, but what is the lesson? One is that we should not necessarily ignore something just because it cannot be found in the data. Much of the empirical work prior to the crisis involved data from the early 1980s to the present (due to an assumption of structural change around that time), sometimes the data goes back to 1959 (when standard series on money end), and occasionally empirical work will use data starting in 1947. So important, infrequent events like the great Depression are rarely even in the data we use to test our models. Things that help to explain this episode may not seem important in limited data sets, but we ignore these possibilities at our own peril.
But how do we know which things to pay attention to if the data isn't always the best guide? We can't just say anything is possible no matter what the data tell us, that's not much of a guide on where to focus our attention.
The data can certainly tell us which things we should take a closer look at. If something is empirically important in explaining business cycles (or other economic phenomena ), that should draw our attention.
But things that do not appear important in data since, say, 1980 should not necessarily be ignored. This is where history plays a key role in directing our attention. If we believe that a collapse of financial intermediation was important in the Great Depression (or in other collapses in the 1800s), then we should ask how that might occur in our models and what might happen if it did. You may not find that the Bernanke, Gertler, Gilchrist model is important when tested against recent data, but does it seem to give us information that coincides with what we know about these earlier periods? We can't do formal tests in these cases, but there is information and guidance here. Had we followed it -- had we remembered to test our models not just against recent data but also against the lessons of history -- we might have been better prepared theoretically when he crisis hit.
Comments, observations and thoughts from two bloggers on applied statistics, higher education and epidemiology. Joseph is an associate professor. Mark is a professional statistician and former math teacher.
Monday, April 18, 2011
Mark Thoma makes an important point
From Economist's View:
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