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A0592
Title: Quasi-score matching estimation for spatial autoregressive models with random weights matrix and regressors Authors:  Tao Zou - The Australian National University (Australia) [presenting]
Abstract: Due to the rapid development of social networking sites, the spatial autoregressive (SAR) model has played an important role in social network studies. However, the commonly used quasi-maximum likelihood estimation (QMLE) for the SAR model is not computationally scalable as the network size is large. In addition, when establishing the asymptotic distribution of the parameter estimators of the SAR model, both weights matrix and regressors are assumed to be nonstochastic in classical spatial econometrics, which is perhaps not realistic in real applications. Motivated by the machine learning literature, quasi-score matching estimation for the SAR model is proposed. This new estimation approach is still likelihood-based, but significantly reduces the computational complexity of the QMLE. The asymptotic properties of parameter estimators under the random weights matrix and regressors are established, which provides a new theoretical framework for the asymptotic inference of the SAR type models. The usefulness of the quasi-score matching estimation and its asymptotic inference are illustrated via extensive simulation studies.