Theory & Prediction Development (car-theory-development)

SkillCommerce & finance

Use when building the conceptual engine of a Contemporary Accounting Research (CAR) manuscript, the economic or behavioral mechanism, predictions/hypotheses, or the formal model, adapted to whether the paper is archival, experimental, analytical, or qualitative. Builds the argument; it does not run estimation (car-data-analysis) or frame the contribution (car-contribution-framing).

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Theory & Prediction Development (car-theory-development) skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/awesome-journal-skills in Contemporary-Accounting-Research-Skills/skills/car-theory-development/SKILL.md and read by ahel’s review.

When to trigger

  • Predictions are descriptive ("X is associated with Y") with no mechanism
  • An analytical paper has equations but no clear economic intuition
  • An experiment lacks a theory that pins down the predicted direction and the process
  • A reviewer says "this is atheoretical" or "the predictions don't follow from the framework"

CAR develops theory differently by tradition

Because CAR is method-agnostic, "theory development" means different things across its traditions, and a reader should never be able to swap in a generic management-theory template:

  • Archival / capital-markets. Ground predictions in economics-based frameworks (information asymmetry and disclosure, agency and contracting, market efficiency and the properties of accounting numbers). Derive directional, falsifiable predictions and, critically, predict cross-sectional variation — the moderators that make the effect stronger or weaker are where the theoretical content lives. State the maintained assumptions linking the accounting construct to the market outcome.
  • Experimental. Specify the psychological/economic process (e.g., motivated reasoning, mental accounting, ambiguity, incentives) that produces the effect, then design predictions that isolate that process — including a predicted mediator and the conditions under which the effect reverses or vanishes. Theory must justify the manipulation, not just the dependent variable.
  • Analytical / modeling. The model is the theory. State the setting, players, information structure, timing, and equilibrium concept; derive results as propositions with proofs; and translate each comparative static into an empirical or institutional implication. The contribution is the economic insight, not the algebra.
  • Field / qualitative. Build theory inductively from the data; make the abductive logic from observations to constructs explicit and traceable.

Predictions and hypotheses

  • Write each prediction so the data could falsify it; state sign and, where possible, relative magnitude.
  • For mediation/process claims, theorize the mechanism before testing it.
  • Distinguish the maintained assumptions (untested) from the tested predictions.

Checklist

  • The mechanism is named and its logic is explicit, not assumed
  • Predictions are directional and falsifiable; cross-sectional/conditional predictions stated
  • (Analytical) assumptions, equilibrium concept, and the intuition behind each result are stated
  • (Experimental) the predicted process/mediator and reversal conditions are specified
  • Predictions map cleanly to constructs the chosen method can measure or manipulate

Anti-patterns

  • Association dressed as theory: "we expect X relates to Y" with no why.
  • Algebra without intuition (analytical) or DV-only theory (experimental) that ignores the process.
  • Borrowed-template theory that ignores accounting's information/contracting context.

Operating pass for Contemporary Accounting Research

Use this as a second-pass capability check. First lock the accounting construct, setting, identification or theory, and disclosure/market/organizational implication; then test whether the manuscript addresses accounting reviewers who expect accounting-specific constructs, credible design, and contribution to reporting, auditing, tax, or governance debates.

  • Primary move: Return a claim-evidence-risk ledger; every recommendation must point to a manuscript location or missing artifact.
  • Decision ledger: return claim / evidence / blocker / next edit rows so the next pass can patch the manuscript directly.
  • Neighbor test: compare against The Accounting Review for US flagship breadth, JAR for Chicago-style accounting research, JAE for economics/accounting interface; if the neighboring outlet has the stronger audience claim, recommend re-routing before polishing.
  • Verification floor: before submission-ready advice, re-open resources/official-source-map.md for volatile rules and name the one unresolved fact that could change the recommendation.

Output format

【Tradition】archival / experimental / analytical / qualitative
【Mechanism】the economic/behavioral logic ...
【Predictions/Hypotheses】H1..Hn, signs, conditional/cross-sectional ...
【Assumptions】maintained vs. tested (or model primitives) ...
【Process】predicted mediator / equilibrium intuition ...
【Next step】car-literature-positioning or car-methods

Signals

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skill
Key
car-theory-development
Source
github.com/brycewang-stanford/awesome-journal-skills