A State-Space Approach for Time-Series Prediction of an Heterogeneous Agent Model

In this paper we apply the state-space model approach to evaluate and compare the forecasting performance of a small-scale heterogeneous agent model (HAM) with fundamentalists and contrarians. As in the tradition of HAMs, agents are heterogeneous in the expectations formation and forecast future prices based on the deviations of previous values with respect to the fundamental value. Moreover, our agents have two specifications for the asset's fundamental value, formalized as a random walk (RW) or with the Gordon model (GM). We examine the models' performance at various forecast horizons (short vs. long horizon) and different frequency-time (monthly and quarterly). Overall, GM statistically outperforms RW specification at the long horizon with statistical significance, while RW and GM are statistically indifferent in the short horizon.