The application of an ensemble of boosted elman networks to time series prediction : a benchmark study

In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Returns to scale, market power, and the nature of price rigidity in new keynesian models with self-fulfilling expectations

Increasing returns to scale and firms' market power are two potential sources of sunspot expectations in neoclassical models. We show that in New Keynesian models, returns to scale and market power can have fundamentally different implications for broad macroeconomic issues, including self-fulfilling expectations, depending on the nature of price rigidity. Our findings suggest that the design of stabilization monetary policy can depend on precise knowledge about the economy's real and nominal features. Therefore, a clear understanding of the specific economic environment and its relevance to monetary policymaking for ensuring macroeconomic stability can be an integrated part of monetary policy practice.