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Activity Number: 480 - Model Testing and Prediction
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #330006 Presentation
Title: Partially Specified Spatial Autoregressive Model with Artificial Neural Network
Author(s): Wenqian Wang* and Beth Andrews
Companies: Northwestern University and Northwestern University
Keywords: spatial autoregressive; neural network; semiparametric; non-Gaussian

The spatial autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical spatial autoregressive model is a linear model for describing spatial correlation. In this work, we expand the classical model to include related exogenous variables, possibly non-Gaussian, high volatility errors, and a nonlinear neural network component. The nonlinear neural network component allows for more model flexibility - the ability to learn and model nonlinear and complex relationships. We use a maximum likelihood approach for model parameter estimation. We establish consistency and asymptotic normality for these estimators under some standard conditions on the spatial model and neural network component. We investigate the quality of the asymptotic approximations for finite samples by means of numerical simulation studies. For illustration, we include a real world application.

Authors who are presenting talks have a * after their name.

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