New analysis

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/29-quarcs-lab-project20XXy/dot-claude/skills/new-analysis

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Install

npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill new-analysis

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Scaffolds a method-specific analysis notebook (DiD, IV, RDD, LASSO, Panel FE) with boilerplate. Use when starting a new econometric analysis.

SKILL.md

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Scaffold Analysis Notebook

Create a new notebook pre-populated with method-specific boilerplate for a common econometric technique.

Arguments

$ARGUMENTS — the method name and optional title (e.g., "DiD Event Study", "IV Analysis of Colonial Origins", "RDD Minimum Wage", "LASSO Variable Selection", "Panel FE Growth Regressions")

Steps

Parse the method from the arguments. Recognized methods:
- DiD (difference-in-differences)
- IV (instrumental variables)
- RDD (regression discontinuity design)
- LASSO (regularized regression / variable selection)
- Panel FE (panel fixed effects)
- If the method is not recognized, ask the user to clarify.
Follow the same notebook creation conventions as /project:new-notebook:
- Check notebooks/ for existing files to determine the next sequential number
- Ask the user for the kernel: Python, R, or Stata
- Create the .ipynb with the appropriate kernel and setup cell:
  - Python: import sys; sys.path.insert(0, ".."); from config import set_seeds, DATA_DIR; set_seeds()
  - R: source("../config.R"); set_seeds()
  - Stata: clear all followed by set seed 42
Add method-specific sections as markdown and code cells:

All methods include these sections:
- Data Loading (code cell)
- Variable Construction (code cell)
- Summary Statistics (code cell with #| label: tbl-<method>-sumstats)
- Estimation (code cell with #| label: tbl-<method>-main)
- Visualization (code cell with #| label: fig-<method>-main)
- Robustness Checks (markdown header + empty code cell)
Method-specific boilerplate:
- DiD: parallel trends test, event study plot (#| label: fig-event-study), TWFE regression, staggered treatment note
- IV: first-stage regression, reduced-form, 2SLS estimation, weak instrument diagnostics (F-statistic, Anderson-Rubin), overidentification test stub
- RDD: running variable histogram, McCrary density test, bandwidth selection (Imbens-Kalyanaraman), local polynomial estimation, RD plot (#| label: fig-rd-plot)
- LASSO: cross-validation for lambda, coefficient path plot (#| label: fig-lasso-path), selected variables, post-LASSO OLS
- Panel FE: within estimator, entity and time FE, clustered standard errors, Hausman test (FE vs RE)

Create the Jupytext .md pair:

uv run jupytext --set-formats ipynb,md:myst notebooks/<name>.ipynb

- notebook: notebooks/<name>.ipynb
  title: "N<number>: <title>"