The $42,000 question regarding any set of regression estimates is whether they have a causal interpretation. This is no less true for quantile regression than Ordinary Least Squares. Suppose we are interested in estimating the effect of a training program on earnings. OLS regression estimates measure the impact of the program on average earnings while quantile regression estimates can be used to measure the impact of the program on median earnings. In both cases, we must worry about whether the estimated program effects are contaminated by omitted variables bias.
Here too, omitted variables problems can be solved using instrumental variables, though IV methods for quantile models are a relatively new development, and are not yet as flexible as conventional 2SLS... Read More
You might be wondering where the covariates have gone. After all, covariates played a starring role in our earlier discussion of regression and matching. Yet the LATE theorem does not involve covariates. This stems from the fact that when we see instrumental variables as a type of (natural or man-made) randomized trial, covariates take a back seat. If, after all, the instrument is randomly assigned, it is likely to be independent of covariates. Not all instruments have this property, however... Read More
The most important result in regression theory is the omitted variables bias formula: coefficients on included variables are unaffected by the omission of variables when the variables omitted are uncorrelated with the variables included. The propensity score theorem, due to Rosenbaum and Rubin (1983), extends this idea to estimation strategies that rely on matching instead of regression, where the causal variable of interest is a treatment dummy.
The propensity score theorem states that if potential outcomes are independent of treatment status conditional on a multivariate covariate vector, Xi, then potential outcomes are independent of treatment status conditional on a scalar function of covariates, the propensity score, defined as p(Xi) = E[Di|Xi]. Formally, we have
Theorem 3.3... Read More
Fixed effects and differences-in-differences estimators are based on the presumption of time-invariant (or group-invariant) omitted variables. Suppose, for example, we are interested in the effects of participation in a subsidized training program, as in the Dehejia and Wahba (1999) and Lalonde (1986) studies discussed in section (3.3.3). The key identifying assumption motivating fixed effects estimation in this case is
E(y0it&i; Xit, Dit) — E(Y0it&i; Xit), (5.3.1)
where a. i is an unobserved personal characteristic that determines, along with covariates, Xit, whether individual i gets training... Read More
4.2.1 The Limiting Distribution of the 2SLS Coefficient Vector
We can derive the limiting distribution of the 2SLS coefficient vector using an argument similar to that used
in Section 3.1.3 for OLS. In this case, let Vi = Xі s,■
stage, equation (4.1.9). The 2SLS estimator can then be written
where the second equality comes from the fact that the first-stage residuals, (Si — S/), are orthogonal to Vi in the sample. The limiting distribution of the 2SLS coefficient vector is therefore the limiting distribution of Ei V/V/] 1 ^0i Vp. This quantity is a little harder to work with than the corresponding OLS quantity, because the regressors in this case involve estimated fitted values, S/... Read More
The QTE estimator is motivated by the observation that, since the parameters of interest are quantile regression coefficients for compliers, they can (theoretically) be estimated consistently by running quantile regressions in the population of compliers. As always, however, the compliers population is not identifiable; we cannot list the compliers in a given data set. Nevertheless, as in Section 4.5.2, the relevant econometric minimand can be constructed using the Abadie Kappa theorem. Specifically,
as before. The QTE estimator is the sample analog of (7.2.3).
There are a number of practical issues that arise when implementing QTE... Read More