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

Filippo Gusella and Giorgio Ricchiuti

Working Papers - Economics from Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa

Abstract: 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.

Keywords: Heterogeneous expectations; forecasting; RW; Gordon model; state-space model (search for similar items in EconPapers)
JEL-codes: C13 C50 E32 G10 G12 G15 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2022
New Economics Papers: this item is included in nep-for
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