A New Approach to Modeling Endogenous Gain Learning

Marcet, and Nicolini (2003) and Milani (2014) demonstrate within the adaptive learning framework that a forecast error-based endogenous gain mechanism that switches between constant gain and decreasing gain may be more effective than the former alone in explaining time-varying parameters. In this paper, we propose an alternative endogenous gain scheme, henceforth referred to as CEG, that is based on recent coefficient estimates by the economic agents. We then show within a controlled simulation environment that CEG outperforms both constant gain learning as well as the aforementioned switching gain algorithm in terms of mean squared forecast errors (MSFE). In addition, we demonstrate within the context of a New Keynesian model that forecasts generated under CEG perform better in certain dimensions, particularly for inflation data, compared to constant gain learning. Combined with the fact that the proposed gain scheme ports easily to existing likelihood based inferential techniques used in constant gain learning, it is readily applicable to richer, more dynamic economic models.