Forecasting prices in dynamic heterogeneous product markets using multivariate prediction methods

Hedonic modeling is used to measure the product price behavior overall in high-tech markets. In a previous work, we showed the opportunity to extend the simple regression to a state space model evaluating hedonic prices from product prices. We created and tested an online estimation algorithm for those values. In that way, we can study time series of implicit prices for individual components of a range of products. In this paper, we implement and compare the hedonic model forecast performances respect to standard autoregressive models, univariate and multivariate. We find that hedonic values not only give extra information about supply market, but they can improve univariate predictions and in, certain periods, also multivariate ones. We show the correctness of algorithm using online version of it. An agent may predict prices for different products sharing a set of component, by taking into account the structure of production process. An application in a multi-agent supply chain simulation confirms the goodness of algorithm to be implemented in a future framework for online price analysis and prediction.