tar-methods

Research Design & Identification (tar-methods)

When to trigger

• Your treatment (a disclosure, a standard adoption, an audit/tax regime) may be endogenous
• Adoption is staggered across firms/years and you need a defensible DiD
• You have an association and a reviewer will ask "is this causal or just correlation?"
• You are designing an experiment to isolate a channel archival data cannot separate
• You are building an analytical model and need to fix primitives and solution concept

TAR is method-agnostic but identification-obsessed

TAR's stated policy is open to all rigorous methods; the bar is the contribution. In the dominant

large-sample archival lane, "rigorous" almost always means a **credible identification

strategy**, because accounting treatments (disclosure choices, conservatism, auditor selection, tax

positions) are rarely randomly assigned. Pick the design that breaks the endogeneity for your

accounting setting.

Identification toolkit for archival accounting

| Identification threat / setting | Design |

|----------------------------------------------------------|--------------------------------------------------------------|

| Regulation / standard adoption with a clean date | Difference-in-differences; event study around the date |

| Staggered adoption across firms/states/countries | Staggered DiD with modern estimators (avoid the TWFE bias) |

| Endogenous accounting/auditor/tax choice | Instrumental variables / 2SLS with a defensible exclusion |

| A threshold rule (covenant, index inclusion, size cutoff)| Regression discontinuity |

| Selection on observables | Matching (PSM/entropy) as a complement, not the main claim |

| A plausibly exogenous shock to information environment | Natural experiment; pre-trends shown |

State the estimating equation, the unit and level, the fixed effects (firm, year,

industry-year), and the identifying variation explicitly. The design section should make a

skeptic believe the variation is as-good-as-random conditional on controls.

If the lane is experimental

• Manipulate the focal accounting construct; use realistic stimuli and an appropriate participant

pool (investors, auditors, managers) — IRB documentation is required and reviewers expect it.

• Pre-register where feasible; include manipulation and attention checks; power the design for the

interaction, not just the main effect.

If the lane is analytical

• Fix the information structure, players, and payoffs before solving; state the equilibrium concept.
• Show the model is the minimal structure that generates the accounting result.

Design hygiene

• Show parallel pre-trends for any DiD; report dynamic (event-time) effects.
• Defend the exclusion restriction for any IV — relevance is not enough.
• Pre-commit the main specification; relegate alternatives to robustness (see tar-data-analysis).
• Plan the data-authenticity trail now: the processing code for the sample is part of submission.

Execution bridge (StatsPAI / Stata MCP)

For the empirical / causal lane, estimate and audit rather than only specify. Full

map: [execution-with-mcp](../../../shared-resources/empirical-methods/execution-with-mcp.md). TAR is archival accounting — DiD around regulation / standard changes, IV, and earnings-based designs; the corporate-causal chain fits directly.

• 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](../../../Journal-of-Finance-Skills/resources/worked-examples/02-execution-walkthrough.md).

Checklist

• [ ] The identifying variation (shock/setting/threshold) is named and defended
• [ ] Estimating equation, unit, level, and fixed effects are stated
• [ ] DiD designs show pre-trends and dynamic effects; staggered designs use a modern estimator
• [ ] IV exclusion restriction is argued, not asserted; matching is a complement, not the claim
• [ ] Experiments have IRB, realistic stimuli, manipulation/attention checks, and adequate power
• [ ] Analytical models fix primitives and the solution concept before solving

Anti-patterns

• Kitchen-sink controls standing in for identification ("we control for everything").
• TWFE on staggered adoption without addressing heterogeneous-treatment-effect bias.
• IV by convenience: an instrument that fails exclusion (correlated with the outcome directly).
• Matching as causal proof when selection is on unobservables.
• An experiment with no IRB or with a participant pool unfit for the construct.

Output format

【Lane】archival / experiment / analytical
【Setting & identifying variation】...
【Design】DiD / staggered-DiD / IV / RDD / event study / experiment / model
【Spec】equation; unit/level; fixed effects; clustering plan
【Identification defense】pre-trends / exclusion / discontinuity / randomization ...
【Data-authenticity plan】processing code + data description ready? yes/no
【Next step】tar-data-analysis