Goodwill and target MQ

This analysis asks whether an acquired target's pre-deal MQ is associated with the goodwill reported by the acquirer.

Manuscript pipeline incomplete

The Python builder creates the analysis dataset and embeds coefficient summaries in JSON, but it does not save fitted models or write a LaTeX table.

flowchart LR
    data[(Goodwill and target MQ)]
    fit[[Run pooled and FE regressions]]
    json[(Coverage and regression summary JSON)]
    latex[/Planned LaTeX table/]

    data --> fit --> json -.-> latex

    classDef input fill:#eee5f5,stroke:#76528c,stroke-width:2px
    classDef code fill:#f3f3f3,stroke:#666,stroke-width:2px
    classDef support fill:#fff0cc,stroke:#a66b00,stroke-width:2px
    classDef tableGap fill:#e2f2e7,stroke:#a66b00,stroke-width:2px,stroke-dasharray:5 5
    class data input
    class fit code
    class json support
    class latex tableGap

The current builder estimates log goodwill on target MQ measured at t-1 and over the three pre-deal years. Its specification grid includes pooled, year, industry, industry-plus-year, acquirer, and acquirer-plus-year models, with additional acquirer-size variants. Standard errors are heteroskedastic-robust or clustered by acquirer when available.

Input: Goodwill and target MQ data Current implementation: shared/data/build_datasets/goodwill_target_mq.py

python -m shared.data.build_datasets.goodwill_target_mq \
  --goodwill-path shared/data/external/goodwill_xbrl_20102022.csv \
  --fuzzy

The command writes the deal-level Parquet output and shared/data/build_datasets/outputs/goodwill_target_mq_summary.json unless overridden. A manuscript-ready implementation should separate estimation from data construction, persist model or tidy numeric results, and write a table fragment under /artifacts/<coverage>/analyses/.