Finite Horizon Learning

Incorporating adaptive learning into macroeconomics requires assumptions about how agents incorporate their forecasts into their decision-making. We develop a theory of bounded rationality that we call finite-horizon learning. This approach generalizes the two existing benchmarks in the literature: Eulerequation learning, which assumes that consumption decisions are made to satisfy the one-step-ahead perceived Euler equation; and infinite-horizon learning, in which consumption today is determined optimally from an infinite-horizon optimization problem with given beliefs. In our approach, agents hold a finite forecasting/planning horizon. We find for the Ramsey model that the unique rational expectations equilibrium is E-stable at all horizons. However, transitional dynamics can differ significantly depending upon the horizon.

Future of the Clean Development Mechanism in Tackling Climate Change

Using a theoretical framework, we explain the impact of the Clean Development Mechanism (CDM) on emissions in Annex I and non-Annex I countries. We show that on one hand, emissions in the non-Annex I country decline because of abatement sponsored by the Annex I country under the CDM; on the other hand, emissions may increase because (i) the Annex I country increases emissions in its own country, and (ii) the non-Annex I country crowds out the bene ts from the CDM projects by increasing its domestic emissions. For the CDM to be e¤ective in reducing global emissions, we show that partial Certi ed Emissions Reduction credits should be given to the Annex I country that sponsors CDM projects in the non-Annex I country. We also suggest that the CDM Executive Board should not allow the CDM projects to be hosted by non-Annex I countries that are too conscious about their emission levels.