A0725
Title: Learning network with focally sparse structure
Authors: Victor Chernozhukov - MIT (United States)
Chen Huang - Aarhus University (Denmark) [presenting]
Weining Wang - University of York (United Kingdom)
Abstract: Network connectedness with a focally sparse structure is studied. We uncover the network effect with a flexible sparse deviation from a predetermined adjacency matrix. More specifically, the sparse deviation structure can be regarded as latent or as misspecified linkages to be estimated. To obtain high-quality estimators for the parameters of interest, we propose using a debiased-regularized, high-dimensional generalized method of moments (GMM) framework. Moreover, this framework also enables us to conduct inference on the parameters. Theoretical results on consistency and asymptotic normality are provided, while accounting for general spatial and temporal dependency of the underlying data-generating processes. Simulations demonstrate a good performance of our proposed procedure. Finally, we apply the methodology to study the spatial network effect of stock returns.
