An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination

Abstract

AI coding agents, general purpose assistants that write and execute code, make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. This paper adapts an open-source agent-loop architecture to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, independent agent searches find methods that improve on benchmarks from the original study. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries.