Weights are a staple of empirical work, yet analytic weights (aweights) often suffer from a bit of an identity crisis. They are often misunderstood as a “one-size-fits-all” solution for data that feel weighted or misapplied as a substitute for sampling weights (pweights) when researchers want to adjust for population representation. In this post, we want to clarify this confusion and discuss the when, how, and why of using aweights in your research. Read more…
Two-step estimation problems can be solved using the gmm command.
When a two-step estimator produces consistent point estimates but inconsistent standard errors, it is known as the two-step-estimation problem. For instance, inverse-probability weighted (IPW) estimators are a weighted average in which the weights are estimated in the first step. Two-step estimators use first-step estimates to estimate the parameters of interest in a second step. The two-step-estimation problem arises because the second step ignores the estimation error in the first step.
One solution is to convert the two-step estimator into a one-step estimator. My favorite way to do this conversion is to stack the equations solved by each of the two estimators and solve them jointly. This one-step approach produces consistent point estimates and consistent standard errors. There is no two-step problem because all the computations are performed jointly. Newey (1984) derives and justifies this approach. Read more…