Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis

Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all combinations of possible controls as well as specified functional form issues, variable definitions, standard error calculations, and estimation commands. This allows analysts to present their core, preferred estimate in the context of a distribution of plausible estimates. Second, we develop a model influence analysis showing how each model ingredient affects the coefficient of interest. This shows which model assumptions, if any, are critical to obtaining an empirical result. We demonstrate the architecture and interpretation of multimodel analysis using data on the union wage premium, gender dynamics inmortgage lending, and tax flightmigration among U.S. states. These illustrate how initial results can be strongly robust to alternative model specifications or remarkably dependent on a knife-edge specification.