tar-methods

name	tar-methods
description	Use when the research design and identification strategy are the bottleneck for a The Accounting Review (TAR) manuscript — choosing the setting, shock, and design that credibly identify an accounting effect, or structuring an analytical model or experiment. Designs the study; it does not run the estimation and robustness (tar-data-analysis) or frame the contribution (tar-contribution-framing).

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. 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.

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