We identify statistical “matches” for missing individual observations in cross-section waves to construct or complete pseudo-panels. Unlike propensity score matching, our method is applicable even when a classifier outcome (e.g., treatment status) is not observed. In non-panel cross-sections, agents are assessed as similar relative to several observable characteristics, which are optimally aggregated. This is model-free, unlike “cohort”, “synthetic variable”, and other imputation methods. Observed covariates are employed as surrogates, not cohort averages or synthetic variables that must satisfy a given model. Our aggregate score is “information efficient”, utilizing all of the probability laws generating the observed variables. Applications include panel construction, network membership, treatment effects, and missing data. We examine private return to R&D in the presence of spillovers using macropanels, and female labor force participation using micropanel data (PSID).