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

Author

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  • Minchul Shin

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.

Suggested Citation

  • Minchul Shin, 2026. "An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination," Working Papers 26-41, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:103699
    DOI: 10.21799/frbp.wp.2026.41
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    Keywords

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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