downstream impact modeling

This paper addresses three core challenges in policy risk forecasting: the difficulty of integrating narrative scenario analysis with quantitative statistical models (e.g., DSGE or VAR), incompleteness of scenario sets, and the lack of rigorous quantification of scenario support. To this end, we propose a novel Bayesian协同 framework featuring three innovations: (1) a formal measure of scenario–model consistency; (2) Bayesian predictive synthesis that coherently integrates expert-judgment-based scenarios with structural econometric models; and (3) probabilistic calibration and synthetic completion of incomplete scenario sets. The method substantially enhances forecast robustness and interpretability, enabling explicit quantification of relative scenario support, hierarchical representation of uncertainty, and dynamic risk communication across multiple scenarios. It delivers a theoretically rigorous yet operationally practical paradigm for policy risk assessment.

This paper addresses the challenge of modeling nonlinear and asymmetric dynamic relationships among macroeconomic and financial variables. We propose the first scenario-analysis-oriented, dynamic nonparametric multivariate Bayesian machine learning framework. Methodologically, we adapt classical econometric tools—including conditional forecasting and generalized impulse response analysis—to high-dimensional Bayesian nonparametric models, integrating dynamic factor extensions and Monte Carlo simulation to enable asymmetric shock response estimation and conditional scenario inference. Our key contribution is the first systematic integration of traditional scenario-analysis tools with nonlinear Bayesian machine learning, explicitly capturing structural asymmetry. The framework is validated across three empirical domains: financial stress testing, macroeconomic risk assessment, and cross-border spillover analysis. Results demonstrate substantial improvements in risk measurement accuracy and cross-jurisdictional early-warning capability, offering a novel paradigm for prudential regulation and policy evaluation.

This paper addresses the challenge of quantifying uncertainty in counterfactual predictions within quantitative trade and spatial models—characterized by dyadic (bilateral) data with complex dependence, few interacting units, and predictions that depend jointly on structural parameter estimates and counterfactual equilibrium inputs. We propose the first Bayesian bootstrap tailored to such models, which simultaneously ensures Bayesian validity in finite samples and asymptotic frequentist consistency—overcoming key limitations of classical bootstrap methods in small-scale, strongly dependent network settings. The method is validated across canonical frameworks including Waugh (2010), Caliendo & Parro (2015), and Artuç et al. (2010), demonstrating robustness and improved reliability in policy-relevant counterfactual inference.

This paper addresses the opacity of policy shock transmission mechanisms in large-scale dynamic macroeconomic models. We propose Transmission Channel Analysis (TCA), a novel framework that unifies graph-theoretic representation with the potential outcomes causal framework, enabling systematic decomposition of total impulse response function (IRF) effects into path-specific effects mediated through well-defined transmission channels. Theoretically, we prove that—under mere structural shock identifiability—the IRF constitutes a sufficient statistic for channel decomposition. TCA is model-agnostic, seamlessly integrating with mainstream frameworks such as SVAR and DSGE, and demonstrates empirical efficacy in attributing monetary, fiscal, and other policy shocks. Relative to existing approaches, TCA dispenses with stringent exogeneity assumptions or auxiliary data requirements, markedly enhancing interpretability of transmission mechanisms and transparency of quantitative attribution. It thus establishes a new paradigm for rigorous, mechanism-aware policy evaluation.

This paper examines how individuals’ subjective beliefs about future income moderate the impact of tax policy on current consumption and saving decisions. Addressing the limitation of conventional policy evaluation—its neglect of expectation heterogeneity—the study establishes, for the first time, theoretical equivalence conditions between regression estimation and structural average partial effects, and proposes a three-step feasible estimator leveraging subjective belief data to jointly model belief measurement and structural policy effect identification. Methodologically, it integrates regression modeling, structural causal inference, and counterfactual prediction frameworks. Empirical analysis using Italian microsurvey data reveals that income expectations significantly attenuate or amplify the consumption response to tax changes; ignoring such beliefs leads to systematic policy effect misestimation exceeding 20%. The study thus provides a replicable methodological paradigm for expectation-driven macro-fiscal policy evaluation.

This study addresses the quantification of the impact of different climate scenarios on expected credit losses (ECL) for financial assets. To this end, it proposes an operational framework for measuring scenario-induced impacts by leveraging existing provisioning systems within financial institutions. The approach adjusts probabilities of default to reflect climate-related shocks and integrates mappings of risk drivers with standardized exposure grouping methodologies, thereby enabling comparable scenario analyses across institutions. The framework provides both a theoretical foundation and a practical implementation pathway for regulators conducting standardized climate stress tests. It has been successfully applied in the 2024 joint climate scenario analysis conducted by the Office of the Superintendent of Financial Institutions Canada and the Autorité des marchés financiers du Québec.

This paper aims to improve real-time forecasting accuracy of the full conditional distribution of macroeconomic variables to systematically characterize macroeconomic risk. Methodologically, it proposes a dynamic forecasting framework that integrates high-dimensional statistical learning, shrinkage regularization, and rolling-window out-of-sample validation, while systematically comparing linear and nonlinear machine learning models. The key contribution is the first incorporation of strict out-of-sample validation directly into the shrinkage estimation procedure—thereby mitigating overfitting and achieving an optimal bias–variance trade-off. Empirical results demonstrate substantial gains in distributional forecast accuracy, confirming the critical role of regularization in modeling high-dimensional macroeconomic data. Moreover, nonlinear models yield only marginal improvements over linear alternatives, underscoring the advantages of structural parsimony and robustness in macroeconomic forecasting.

Large language models (LLMs) struggle to effectively model macroeconomic dynamics under small-sample regimes. Method: This paper proposes a novel paradigm—“theory-driven synthetic data + temporal large models”—where million-scale, theory-consistent synthetic panel data are generated via dynamic stochastic general equilibrium (DSGE) modeling and Bayesian posterior sampling, then used to train a temporal Transformer architecture. Contribution/Results: The approach synergistically integrates the structural rigor of macroeconomic theory with the representational power of LLMs, enabling the model to learn a generalizable “macroeconomic language.” Empirical results demonstrate that the resulting hybrid predictor significantly outperforms both purely statistical models and conventional DSGE models in forward-looking forecasts through 2025, achieving, for the first time, a unified balance between theoretical interpretability and data-driven predictive accuracy.

This study addresses the lack of systematic modeling approaches for token economies and quantitative analysis of event impacts. Building upon the DeTEcT framework, it proposes the first integrated methodology that combines formal token economy simulation with significance-based measurement of event effects. By introducing an event impact analysis framework augmented with numerical simulation techniques and wealth distribution metrics, the work enables quantitative assessment of wealth redistribution effects triggered by endogenous policy changes—such as Bitcoin Improvement Proposals (BIPs). Using Bitcoin as a case study, the approach demonstrates its effectiveness and practicality in capturing economic dynamics and evaluating the consequences of significant protocol-level events.

This study addresses a critical limitation of conventional Cross-Impact Balance (CIB) analysis, which yields only static consistent scenarios and cannot quantify dynamic structural aspects such as transition efforts, key leverage points, timing of adjustments, or responses to external shocks. To overcome this, the authors introduce linear response theory into the CIB framework, exploiting the structural isomorphism between the CIB drift matrix and the Leontief input-output matrix. This enables the derivation of four analytical constructs—Type I cross-impact multipliers, perturbation budgets, impulse response functions, and unit impulse shock profiles—each admitting closed-form solutions that characterize indirect effects, transition resistance, dynamic adjustment pathways, and network sensitivity in socio-technical systems. Applied to an energy transition case, the approach successfully computes all dynamic indicators across five structural equilibria, offering a transferable quantitative toolkit for assessing system resilience, designing transition pathways, and informing policy interventions.