DSGE and Accountability for Policy Evaluation: “Liability Boundary (Scope/Non-claim)” for Specifying Linearized and Approximate Rights

While DSGE serves as a foundational language in macroeconomics, policy decisions are discrete, finite, and irreversible. This article proposes “Responsibility Boundaries,” “Finite Closure,” and “ADIC” to bridge the gap between asymptotic theory and decisional accountability.

1. The problem is not the predictive power of DSGE but the specification: Defining accountability through scope/non-claims

1.1 The Reality of Decisional Finality: Infinite Horizons Do Not Exist

Economic theory often operates within the elegance of long-term equilibria and steady states. However, the reality of policy-making is defined by “The Now” — discrete moments where action is required under rigid deadlines.

• The Weight of “One-off” Decisions: Monetary policy committees and fiscal authorities do not have the luxury of infinite trials. A decision to adjust interest rates or implement a stimulus is a discrete event with irreversible repercussions, occurring within a specific, narrow window of opportunity.

• Irreversibility and Systemic Externality: The cost of reversing fiscal measures or structural regulations is prohibitive. Once executed, these decisions propagate through the economic system, creating path dependencies that cannot be easily unwound.

In this high-stakes environment, the primary requirement for a model is not merely to be “statistically plausible” on average, but to provide an explicit specification: What does this model guarantee, and what does it explicitly exclude, for this specific, singular decision? Without such delineations, accountability remains elusive.

1.2 Shifting from “Forecasts” to “Specifications”

Historically, the evaluation of economic models has been fixated on “predictive accuracy” (ex-post forecasting). However, for a model to function as a reliable institutional instrument, it requires a “Responsibility Boundary.”

A Responsibility Boundary is a formal specification that defines the domain explicitly warranted by the model and, crucially, the “Non-claims” (the areas where the model provides no guarantee) during a specific decision-making event. This is not a critique of the DSGE framework itself, but an essential engineering requirement for its responsible application.

2. DSGE as an Institutional Bedrock: Utility and Its Theoretical Limits

2.1 DSGE as the “Lingua Franca” of Policy Evaluation

The continued dominance of DSGE models, despite various criticisms, stems from their role as a standardized protocol for discourse rather than a claim to absolute truth.

• Theoretical Rigor: By incorporating microfoundations that withstand the Lucas Critique, DSGE provides a consistent “common ground” for comparing disparate policy scenarios.

• A Protocol for Transparency: It serves as a vital institutional mechanism, allowing policy-making processes to be scrutinized through objective, quantified frameworks. Currently, no comparable alternative provides the same level of structural consistency.

2.2 The Epistemic Gap: Asymptotic Justification vs. Finite Execution

The tension arises because the justification for DSGE models often rests on asymptotic properties — theoretical behaviors that emerge as sample sizes approach infinity or as shocks average out over a long horizon.

Furthermore, the practical application of DSGE almost universally relies on linearization. We solve for non-linear, complex realities by applying massive simplifications. While a model may be “theoretically robust in the limit,” it may lack a defined safety margin for a single-instance judgment within a finite time window. This is the “undefined territory” where institutional risk resides.

3. The Proposal: Operationalizing “Responsibility Boundaries”

Recognizing the utility of DSGE, we propose a paradigm shift. We must demand that every model used for policy intervention include a Responsibility Boundary Specification at the moment of decision.

“We do not question the empirical usefulness of DSGE models. We question their lack of a finite responsibility boundary at the moment of decision.”

To elevate DSGE from an “interpretive lens” to an “accountable technology,” we must close the loophole of “long-term convergence” and define the model’s performance within the actual timeframe of the policy’s impact.

3.1 The Architecture of Responsibility: Scope and Non-claims

We propose that every model output be accompanied by a “Model Specification Certificate” consisting of:

Scope (The Warranted Domain)

• State Space $\Omega$: The specific range of economic variables (e.g., inflation rates, output gaps) within which the model is calibrated and valid.

• Certified Error Bound $\epsilon_{\rm cert}$: An aggregated upper bound of uncertainty, encompassing linearization errors, estimation noise, and numerical implementation artifacts.

• Certified Output Interval: Ensuring that the true value $g_{\rm true}$ is contained within the specified range: $g_{\rm true} \in [\hat{g} — \varepsilon_{\rm cert}, \hat{g} + \varepsilon_{\rm cert}]$.

Non-claims (Explicit Disclaimers)

• Structural Ruptures: Explicitly stating that the model is non-guaranteed in the event of regime shifts or “Black Swan” market dislocations.

• Extrapolation Limits: Disclaiming any validity for forecasts that exceed the specified observation window $T$.

• Exogenous Variables: Clarifying that the model does not account for political implementation delays or other non-modeled externalities.

4. The GhostDrift Framework: Finite Closure and ADIC

To transition from theory to institutional practice, we introduce two core technical concepts:

4.1 Finite Closure

Finite Closure is the mathematical act of intentionally “bounding” the model’s applicability to a finite set — specifically a defined state space ($|x| \le R$) and a fixed time horizon ($t_0+H$). By declaring the model’s silence on anything outside these bounds, we eliminate the ambiguity of “infinite-horizon” justifications and focus accountability on the immediate decisional window.

4.2 ADIC (Audit-ready Deterministic Index of Calculation)

ADIC represents a shift from “Trust-based Modeling” to “Verification-based Modeling.” It functions as a computational ledger that records every step of the model’s execution — including linearization, filtering, and numerical approximation. By documenting error propagation in real-time, ADIC allows for the objective verification of the $\epsilon_{\rm cert}$ by independent third parties.

5. Implementation: The Model Specification Certificate

In practice, a DSGE model would generate a “Responsibility Certificate” in a machine-readable format (e.g., JSON), ensuring that policy recommendations are inseparable from their operational limits.

{  "model_id": "DSGE-Refined-Linear-v2",  "operational_envelope": {    "state_domain": "||x|| <= R (Standard Volatility)",    "temporal_horizon": "Q1 to Q8"  },  "certified_error_budget": {    "linearization_delta": 0.012,    "parameter_uncertainty": 0.020,    "total_epsilon_cert": 0.035  },  "non_claims": [    "regime_shift_sensitivity",    "unmodeled_fiscal_delays"  ],  "audit_ledger_hash": "sha256:7f83b..."}

By integrating this certificate into policy dossiers, the model becomes more than a simulation; it becomes verifiable evidence that delineates the precise boundaries of its own authority.

6. Conclusion: Evolution, Not Eradication

The objective is not to discard the DSGE framework, but to mature it. We must bridge the gap between its sophisticated theoretical foundation and the pragmatic requirements of finite, high-stakes decision-making.

By formalizing the Responsibility Boundary (Domain $\Omega$, Window $T$, Error $\epsilon_{\rm cert}$), we transform DSGE into an “Accountable Institutional Technology.” The adoption of Finite Closure and ADIC represents the necessary evolution for economic modeling to meet the modern demands for transparency, safety, and decisional integrity.

▼We have published an audit model for electricity demand that can be directly applied to DSGE.

https://ghostdrifttheory.github.io/adic-certificate-audit/