Jme Data Analysis

---
name: jme-data-analysis
description: Use when building or stress-testing the empirical/quantitative analysis for a Journal of Monetary Economics (JME) manuscript — VAR/SVAR, local projections, DSGE estimation, moment matching, IRFs, and FEVDs — to monetary-economics and macroeconomics norms. Covers estimation choices, inference, and robustness.
---

# Data & Quantitative Analysis (jme-data-analysis)

## When to trigger

- The estimation runs but referees will question the specification or inference
- You must decide between a VAR, a proxy-SVAR, and local projections
- A DSGE is estimated/calibrated and needs convergence and fit diagnostics
- The robustness battery for a macro paper is unclear

## Macro-empirical norms at JME

JME analysis is **aggregate and policy-relevant**, so the workhorses are different from micro-econometrics. The core toolkit:

- **VAR / SVAR / proxy-SVAR** for dynamic responses to identified shocks; report **impulse responses with confidence/credible bands**, lag selection, stability, and **forecast-error variance decompositions (FEVDs)**.
- **Local projections (Jordà)** as a robustness counterpart to VAR IRFs; show both when feasible, since LP trades variance for robustness to misspecification.
- **DSGE / quantitative models** estimated by Bayesian methods (Dynare) or calibrated to micro moments; report **prior/posterior plots, MCMC convergence, identification (Iskrev), and posterior predictive / second-moment fit**.
- **Real-time data** (FRED/ALFRED vintages, Greenbook/Tealbook) where the information set matters — using final-revised data to study a real-time policy decision is a known pitfall.

Inference must match the design: **HAC / Newey–West** or clustered standard errors for time-series regressions and local projections; credible intervals from the posterior for Bayesian DSGE; bootstrap or analytical bands for VAR IRFs. Report units consistently — e.g., responses to a **100-basis-point** or **one-standard-deviation** policy shock.

## Robustness battery (macro)

- Alternative lag lengths, sample splits (e.g., pre/post-1984 Great Moderation, ZLB period), and sub-samples
- Alternative identification (ordering, restriction set, instrument) and LP-vs-VAR comparison
- Real-time vs. revised data; alternative shock series
- For DSGE: prior sensitivity, alternative calibrations, and the mechanism on/off comparison
- Zero-lower-bound / effective-lower-bound treatment where the sample spans it

## Checklist

- [ ] IRFs reported with bands; FEVDs where informative
- [ ] LP and VAR compared where the question allows
- [ ] Inference matched to the design (HAC/cluster, posterior bands, bootstrap)
- [ ] Real-time vs. revised data considered
- [ ] DSGE convergence / identification / fit diagnostics reported
- [ ] Shock units stated and consistent across exhibits
- [ ] Robustness pushed to the online supplement to respect the 40-page / ≤10-exhibit cap

## Anti-patterns

- Recursive SVAR ordering presented as the only identification with no robustness
- Using final-revised data to model a real-time policy choice
- Reporting a single DSGE point estimate with no convergence or prior-sensitivity evidence
- IRFs without bands, or with inconsistent shock units across figures

## Output format

```
【Method】VAR / SVAR / proxy-SVAR / LP / DSGE / mixed
【Inference】HAC / cluster / posterior bands / bootstrap
【IRFs + FEVDs】reported? Y/N
【LP-vs-VAR】reported? Y/N/NA
【Real-time data】used where needed? Y/N
【Robustness done / missing】[...]
【Next step】jme-tables-figures
```