A Functional Connectivity Approach for Modeling Cross-Sectional Dependence with an Application to the Estimation of Hedonic Housing Prices in Paris
Georges Bresson, Cheng Hsiao
Advances in Statistical Analysis
#002130 20131014 (published)
This paper proposes a functional connectivity approach, inspired from brain imaging literature, to model cross-sectional dependence. Using a varying parameter framework, the model allows correlation patterns to arise from complex economic or social relations rather than being simply functions of economic or geographic distances between locations. It nests the conventional spatial and factor model approaches as special cases. A Bayesian Markov Chain Monte Carlo method implements this approach. A small scale Monte Carlo study is conducted to evaluate the performance of this approach in finite samples, which outperforms both a spatial model and a factor model. We apply the functional connectivity approach to estimate a hedonic housing price model for Paris using housing transactions over the period 1990-2003. It allows us to get more information about complex spatial connections and appears more suitable to capture the cross-sectional dependence than the conventional methods.
JEL-Codes: C21, C23, C31, C33, R21, R31.
Keywords: Bayesian hierarchical, Functional connectivity, Hedonic housing prices, Panel spatial dependence.

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