Category Mostly Harmless Econometrics: An Empiricist’s Companion

2.5.1 2SLS Mistakes

2SLS estimates are easy to compute, especially since software like SAS and Stata will do it for you. Oc­casionally, however, you might be tempted to do it yourself just to see if it really works. Or you may be stranded on the planet Krikkit with all of your software licenses expired (Krikkit is encased in a slo-time envelope, so it will take you a long time to get licenses renewed). "Manual 2SLS" is for just such emergen­cies. In the Manual 2SLS procedure, you estimate the first stage yourself (which in any case, you should be looking at), and plug the fitted values into the second stage equation, which is then estimated by OLS. Returning to the system at the beginning of this chapter, the first and second stages are

si — Xi^io + ^1iZi + £ii Yi — a’Xi + psi + [pi + p(...

1.4.1 Weighting Regression

Few things are as confusing to applied researchers as the role of sample weights. Even now, 20 years post – Ph. D., we read the section of the Stata manual on weighting with some dismay. Weights can be used in a number of ways, and how they are used may well matter for your results. Regrettably, however, the case for or against weighting is often less than clear-cut, as are the specifics of how the weights should be programmed. A detailed discussion of weighting pros and cons is beyond the scope of this book. See Pfefferman (1993) and Deaton (1997) for two perspectives. In this brief subsection, we provide a few guidelines and a rationale for our approach to weighting.

A simple rule of thumb for weighting regression is use weights when they make it more likely th...

But when you start exercising those rules, all sorts of processes start to happen and you start to find out all sorts of stuff about people… Its just a way of thinking about a problem, which lets the shape of the problem begin to emerge. The more rules, the tinier the rules, the more arbitrary they are, the better.

Douglas Adams, Mostly Harmless (1995)

Regression discontinuity (RD) research designs exploit precise knowledge of the rules determining treat­ment. RD identification is based on the idea that in a highly rule-based world, some rules are arbitrary and therefore provide good experiments. RD comes in two styles, fuzzy and sharp. The sharp design can be seen as a selection-on-observables story. The fuzzy design leads to an instrumental-variables-type setup.

і

^XlYl = (X ‘X )-1X ‘y,

i where X is the NXK matrix with rows Xi and y is the N x 1 vector of Yi’s. We saw in Section 3.1.3 that /3 has an asymptotically Normal distribution. We can write:

VN(3 – /) – n(0, n)

where П is the asymptotic covariance matrix. Repeating (3.1.7), the formula for П in this case is

Пг = E [XiXi]-1^ [XiXief] E [XiXi]-1, (8.1.1)

where ei = Yi—Xi/. When residuals are homoskedastic, П simplifies to fic = a2E[XiXi]~1 where a2 = E[e2].

We are concerned here with the bias of robust standard errors in independent samples (i. e., no clustering or serial correlation). To simplify the derivation of bias, we assume that the regressor vector can be treated as fixed in repeated samples, as it would be if we sampled stratifying on Xi...

A vast literature in social science is concerned with peer effects. Loosely speaking, this means the causal effect of group characteristics on individual outcomes. Sometimes regression is used in an attempt to uncover these effects. In practice, the use of regression models to estimate peer effects is fraught with peril. Although this is not really an IV issue per se, the language and algebra of 2SLS helps us understand why peer effects are hard to identify.

Broadly speaking, there are two types of peer effects. The first concerns the effect of group characteristics such as the average schooling in a state or city on individually-measured outcome variable. This peer effect links the average of one variable to individual outcomes as described by another variable...