Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.17381 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911530778361856 |
|---|---|
| author | Shin, Minchul |
| author_facet | Shin, Minchul |
| contents | AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_17381 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination Shin, Minchul Econometrics Machine Learning AI coding agents make empirical specification search fast and cheap, but they also widen hidden researcher degrees of freedom. Building on an open-source agent-loop architecture, this paper adapts that framework to an empirical economics workflow and adds a post-search holdout evaluation. In a forecast-combination illustration, multiple independent agent runs outperform standard benchmarks in the original rolling evaluation, but not all continue to do so on a post-search holdout. Logged search and holdout evaluation together make adaptive specification search more transparent and help distinguish robust improvements from sample-specific discoveries. |
| title | An Auditable AI Agent Loop for Empirical Economics: A Case Study in Forecast Combination |
| topic | Econometrics Machine Learning |
| url | https://arxiv.org/abs/2603.17381 |