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In this paper we introduce a multilevel specification with stochasticvolatility for repeated cross-sectional data. Modelling the time dynamics inrepeated cross sections requires a suitable adaptation of the multilevelframework where the individuals/items are modelled at the first level whereasthe time component appears at the second level. We perform maximum likelihoodestimation by means of a nonlinear state space approach combined withGauss-Legendre quadrature methods to approximate the likelihood function. Weapply the model to the first database of tribal art items sold in the mostimportant auction houses worldwide. The model allows to account properly forthe heteroscedastic and autocorrelated volatility observed and has superiorforecasting performance. Also, it provides valuable information on markettrends and on predictability of prices that can be used by art marketsstakeholders.

Multilevel Models with Stochastic Volatility for Repeated Cross-Sections: an Application to tribal Art Prices