Transmission Channel Analysis in Dynamic Models

2024-05-29

AI Summary

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.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Large-scale security measurements

Abstract

We propose a framework for analysing transmission channels in a large class of dynamic models. We formulate our approach both using graph theory and potential outcomes, which we show to be equivalent. Our method, labelled Transmission Channel Analysis (TCA), allows for the decomposition of total effects captured by impulse response functions into the effects flowing through transmission channels, thereby providing a quantitative assessment of the strength of various well-defined channels. We establish that this requires no additional identification assumptions beyond the identification of the structural shock whose effects the researcher wants to decompose. Additionally, we prove that impulse response functions are sufficient statistics for the computation of transmission effects. We demonstrate the empirical relevance of TCA for policy evaluation by decomposing the effects of policy shocks arising from a variety of popular macroeconomic models.

Problem

• Analyzing transmission channels in dynamic models using graph theory and potential outcomes
• Decomposing total effects into transmission channel effects for policy evaluation
• Proving impulse response functions suffice for computing transmission effects

Innovation

• Combines graph theory and potential outcomes
• Decomposes effects via Transmission Channel Analysis
• Uses impulse response functions as statistics