| name | jae-methods |
| description | Use when designing the empirical (or analytical) approach for a Journal of Accounting and Economics (JAE) manuscript — choosing an identification strategy (natural experiments, DiD, IV, RD) for large-sample archival capital-markets/contracting/disclosure data, or structuring an economic model, so the design can credibly support the economic prediction. |
Research Design & Identification for JAE (jae-methods)
When to trigger
- You have a prediction but no credible way to rule out endogeneity or reverse causality
- A reviewer will ask "is this causal or just correlation?"
- You must choose between an archival quasi-experiment and an analytical model
- Your treatment (a disclosure, a standard, a contract feature) is plausibly chosen, not random
JAE's dominant methodology
JAE's workhorse is large-sample empirical archival research grounded in economics — observational capital-markets and contracting data analyzed with econometric, identification-focused designs — alongside analytical economic modeling. The journal favors economic analyses of accounting problems (capital-markets information content, contracting, disclosure, agency/monitoring) in the Watts-Zimmerman positive-accounting tradition. It does not publish normative prescriptions, behavioral lab experiments, or design-science artifacts; design accordingly.
Design for identification
Because accounting choices and disclosures are endogenous, a bare panel regression rarely survives review. Match the design to the prediction:
| Setting / claim | Identification strategy |
|---|
| A regulation/standard changes for some firms | Difference-in-differences around the shock; staggered DiD |
| A continuous threshold (covenant, index, size cut) | Regression discontinuity |
| Endogenous regressor, valid instrument available | IV / 2SLS; defend exclusion restriction explicitly |
| Self-selection into disclosure/treatment | Heckman selection; propensity-score matching |
| Information event (earnings, 8-K, disclosure) | Short-window event study (CARs), market-reaction design |
| Pure mechanism / equilibrium claim | Analytical model with assumptions, propositions, proofs |
State the identifying assumption in words (parallel trends, exclusion restriction, continuity at the cutoff) and show how the design satisfies it. A natural experiment from a regulatory shock (SOX, Reg FD, IFRS/ASU adoption, an enforcement change) is the most persuasive JAE design when available.
Sample and measurement design
- Population and sample waterfall: define the population, the merges (Compustat-CRSP-I/B/E/S-Execucomp-DealScan-Audit Analytics via WRDS), and every exclusion, with counts.
- Construct measurement: justify proxies (discretionary accruals, accrual quality, conditional conservatism, bid-ask spread for information asymmetry) and pre-register the expected sign.
- Control set: include the economic determinants the theory implies; avoid "bad controls" that absorb the mechanism.
Analytical-model design
If the contribution is the model: state primitives and the information structure, solve for equilibrium, present comparative statics as testable propositions, and put proofs in an appendix. Keep assumptions economically interpretable.
Referee pre-mortem
Before locking the design, answer the three questions an economics-trained JAE referee asks of every archival accounting paper:
- Who chose the treatment? If the firm chose it (a disclosure, a covenant, an accounting method), what makes the variation you exploit exogenous to the outcome?
- What else moved at the same time? Regulatory shocks bundle provisions; show that the mechanism-specific margin responds, not merely anything measured post-shock.
- Would the estimate survive the firm's best response? Anticipation, selection into treatment, and contract renegotiation can each reverse a naive estimate — state which applies to your setting and how the design absorbs it.
If any answer takes more than three sentences, redesign before drafting.
Execution bridge (StatsPAI / Stata MCP)
For the empirical / causal lane, estimate and audit rather than only specify. Full
map: execution-with-mcp. JAE is empirical accounting with an economics lens; treat identification and weak-IV-robust inference as the binding constraints.
detect_design → recommend → fit with as_handle=true → audit_result to
enumerate the checks the design owes.- Panel / staggered DiD:
callaway_santanna / sun_abraham + bacon_decomposition
honest_did_from_result. IV: effective_f_test + anderson_rubin_ci. RDD:
rdrobust + mccrary_test.
- Experiments: randomization-based inference and
romano_wolf for the many-outcome
family-wise correction reviewers expect.
Match the toolchain to the reviewer pool, and report the effect size the venue
wants. A run end-to-end (synthetic data, real returns) is in the
JF execution walkthrough.
Checklist
Anti-patterns
- "Kitchen-sink" panel OLS presented as if causal.
- Instruments with no credible exclusion restriction.
- Ignoring self-selection into disclosure/treatment.
- Bad controls that mechanically absorb the effect of interest.
- A normative or lab-experiment design that JAE does not publish.
Output format
【Design】DiD / RD / IV / matching / event study / analytical model
【Identifying assumption】parallel trends / exclusion / continuity ...
【Shock or instrument】...
【Sample waterfall】population → merges → exclusions → final N
【Key proxies & expected signs】...
【Threats to identification】... and how addressed
【Next step】jae-data-analysis
