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This paper introduces a spatial panel quantile model with unobserved heterogeneity. The proposed model is capable of capturing high-dimensional cross-sectional dependence and allows heterogeneous regression coefficients. A new procedure is proposed to estimate the model parameters. We establish the asymptotic theory of the estimated parameters under the large-$N$ and large-$T$ scenario. We prove that the widely-used Bai and Ng's information criterion can consistently estimate the dimension of interactive fixed effects. Monte Carlo simulations document the satisfactory performance of the proposed method. We apply our model to study the quantile co-movement structure of the U.S. stock market by taking into account the input-output linkages as firms are connected through the input-output production network.
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