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.

Temper and Temperature: The Missing Link of Climate on Armed Conflicts

We investigate the causes of a conflict by adding ambient climate factors to the existing bundle of most significant variables. It turns out that – controlling for possible associations – temperature could actually induce a conflict. We emphasise that temperature could not be a dominant reason in starting a conflict; however, it could escalate the chances when other factors are present. This paper references some of the related psychological studies to support this claim. We also show that grievance factors could actually be rightfully effective in starting an internal conflict alongside greed based reasons. In the end, we believe that it could be informative to study ambient factors more often in economics.