Abstract:  We propose solutions to two important problems that have received relatively little attention in the field of survey weighting: the construction of population targets in the face of irregularly missing data, and the optimal selection of weighting targets from the set of possible auxiliary variables. Our solution to the first problem relies on a dynamic Bayesian population-interpolation model that allows subpopulation estimates in a given year to be informed by data from other years. To address the second, we formulate the problem of target selection as one of variable subset selection, for which we propose a lasso-based solution. We demonstrate the usefulness of these techniques by using them to generate weights for quota-sampled opinion polls from the early days of survey research. Given the declining response rates, rising use of non-probability samples, and growth in potential sources of auxiliary information in modern-day polling, these methods have wide potential application in contemporary survey research as well. (co-authored with Mallory Wang)