WSJ article on application of instrument variables regression

There is a front page (!) article today (Tuesday Feb 27, 2007) in the
Wall Street Journal by Mark Whitehouse on how economists are applying
the technique of instrumental variables regression. I have heard about
this technique but have not studied it. It's covered in Greene's
"Econometric Analysis". Here are some excerpts from the article.

"Prof. Waldman's use of precipitation illustrates one of the tools
that has emboldened them: the instrumental variable, a statistical
method that, by introducing some random or natural influence, helps
economists sort out questions of cause and effect. Using the
technique, they can create "natural experiments" that seek to
approximate the rigor of randomized trials -- the traditional gold
standard of medical research

But as enthusiasm for the approach has grown, so too have questions.
One concern: When economists use one variable as a proxy for another
-- rainfall patterns instead of TV viewing, for example -- it's not
always clear what the results actually measure. Also, the experiments
on their own offer little insight into why one thing affects another.

"There's a saying that ignorance is bliss," says James Heckman, an
economics professor at the University of Chicago who won a Nobel Prize
in 2000 for his work on statistical methods. "I think that
characterizes a lot of the enthusiasm for these instruments." Says MIT
economist Jerry Hausman, "If your instruments aren't perfect, you
could go seriously wrong."

<snip>

"More broadly, randomized trials seldom lend themselves to studying
economic questions, particularly the more traditional ones. It would
be unfair to randomly subject some people to a higher tax rate just to
see how it affects their spending.

Instead, economists look for instruments -- natural forces or
government policies that do the random selection for them. First
developed in the 1920s, the technique helps them separate cause and
effect. Establishing whether A causes B can be difficult, because
often it could go either way. If television watching were shown to be
unusually prevalent among autistic children, it could mean either that
television makes them autistic or that something about being autistic
makes them more interested in TV.

The ideal instrument is a variable that is correlated with A but has
no direct effect of its own on B. It should also have no connection to
other factors that might cause B. If data in a study nonetheless show
that the instrumental variable is linked to B, it suggests that A must
be contributing to B."

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