Uncertainty, Political Preferences, and Stabilization: Stochastic Control Using Dynamic CGE Models

Traditional computable general equilibrium (CGE) models have ignored uncertainty ─ even when applied to fields such as environmental modeling that are replete with economic uncertainty. In contrast, many control theory models have focused on the effects of uncertainty. Thus marrying the tradition of CGE and control modeling can result in pricequantity models with explicit dynamics and careful treatment of uncertainty. This paper is a next step toward the merger of optimal control models with dynamic CGE models. It demonstrates the usefulness of CGE techniques in control theory application and provides a practical guideline to policymakers in this relatively new field. Moreover, it explores the link between economic stabilization and optimal environmental fiscal policy design in a stochastic dynamic general equilibrium framework. Uncertainty, short-term quantity adjustment process, and sector-specific political preferences (e.g., more stabilization priorities on polluting industries) are taken into account in exploring what time paths of adjustments of the economy would be optimal for the government with explicit policy goals. The optimal control solutions could differ not only due to differences in underlying model assumptions or structures, but also depending crucially on uncertainty about the magnitude of various parameters in the economy. In particular, it is also shown that the performance of economic stabilization could vary significantly with asymmetric political preferences/uncertainty across industrial sectors. In such cases, allowing for those components in more general CGE-based economic modeling may identify policies in the inherently stochastic world that may outperform traditional control-theory (macroeconomic) modeling approaches.