  We propose employing a debiased-regularized, high-dimensional generalized
method of moments (GMM) framework to perform inference on large-scale spatial
panel networks. In particular, network structure with a flexible sparse
deviation, which can be regarded either as latent or as misspecified from a
predetermined adjacency matrix, is estimated using debiased machine learning
approach. The theoretical analysis establishes the consistency and asymptotic
normality of our proposed estimator, taking into account general temporal and
spatial dependency inherent in the data-generating processes. The
dimensionality allowance in presence of dependency is discussed. A primary
contribution of our study is the development of uniform inference theory that
enables hypothesis testing on the parameters of interest, including zero or
non-zero elements in the network structure. Additionally, the asymptotic
properties for the estimator are derived for both linear and nonlinear moments.
Simulations demonstrate superior performance of our proposed approach. Lastly,
we apply our methodology to investigate the spatial network effect of stock
returns.
