A coupling algorithm for computing large-scale dynamic computable general equilibrium models

Abstract

Large-scale dynamic computable general equilibrium (CGE) models are widely used for policy evaluation and economic forecasting in the studies of global environmental problems, such as climate change. Investigating these issues requires that CGE models have long time spans. Widely used dynamic mechanisms in CGE modeling, such as myopic expectations and calibration on `balanced growth' path, are inadequate for such a long time scale. To improve the performances of long-term CGE models, a coupled algorithm that combines CGE modeling with optimal growth modeling approach, is introduced in this paper. The caveats of existing methods and improvements from the coupled algorithm are explored analytically as well as exemplified in the EPPA model, one large-scale dynamic CGE model with long time horizon. Numerical results from the EPPA model show the superiority of the coupled algorithm over existing treatments of dynamics in CGE models.