Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients

  title={Generalized Yule-Walker Estimation for Spatio-Temporal Models with Unknown Diagonal Coefficients},
  author={Baojun Dou and Maria Lucia Parrella and Qiwei Yao},
  journal={arXiv: Methodology},
Banded Spatio-Temporal Autoregressions
A new class of spatio-temporal models with unknown and banded autoregressive coefficient matrices with Yule-Walker equations is proposed, which represents a sparse structure for high-dimensional spatial panel dynamic models when panel members represent economic (or other type) individuals at many different locations.
Sparse Generalized Yule-Walker Estimation for Large Spatio-temporal Autoregressions with an Application to NO2 Satellite Data
We consider sparse estimation of a class of high-dimensional spatio-temporal models. Unlike classical spatial autoregressive models, we do not rely on a predetermined spatial interaction matrix.
Estimation and Inference for Spatial Models with Heterogeneous Coefficients: An Application to U.S. House Prices
This paper considers the estimation and inference of spatial panel data models with heterogeneous spatial lag coefficients, with and without weakly exogenous regressors, and subject to
Spatial Lag Model with Time-lagged Effects and Spatial Weight Matrix Estimation
This paper considers a spatial lag model with different spatial weight matrices for different timelagged spatial effects, while allowing both the sample size T and the panel dimension N to grow to
Multiple Testing for Different Structures of Spatial Dynamic Panel Data Models
This work proposes a strategy for testing the particular structure of the spatial dynamic panel data model by means of a multiple testing procedure that allows to choose between the generalized version of the model and some specific versions derived from the general one by imposing particular constraints on the parameters.
Modelling high-dimensional time series efficiently by means of constrained spatio--temporal models
Many econometric analyses involve spatio--temporal data. A considerable amount of literature has addressed spatio--temporal models, with Spatial Dynamic Panel Data (SDPD) being widely investigated
Estimation and inference for spatial models with heterogeneous coefficients: An application to US house prices
This paper considers the problem of identification, estimation and inference in the case of spatial panel data models with heterogeneous spatial lag coefficients, with and without (weakly) exogenous
A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types
An estimation method is developed based on functional principal component analysis (FPCA), the isometric logratio (ilr) transformation and the maximum likelihood estimation (MLE) method for an SAR model that has the merits of classical functional linear models and compositional linear models with scalar responses.
Spatial modelling and volatility matrix estimation in high dimension statistics with financial applications
High dimension modelling is an important area in modern statistics. For example, a large number of problems that arise in finance are also inspired by more and more available high dimensional data.


Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances
GMM estimation of spatial autoregressive models with unknown heteroskedasticity
Near Unit Root in the Spatial Autoregressive Model
Abstract This paper studies the spatial autoregressive (SAR) model for cross-sectional data when the coefficient of the spatial lag of the dependent variable is near unity. We decompose the data