Generalized Yule-Walker estimation for spatio-temporal models with unknown diagonal coefficients

DOI10.1016/J.JECONOM.2016.05.014zbMath1443.62252arXiv1505.01177OpenAlexW2779734446MaRDI QIDQ308397

Maria Lucia Parrella, Baojun Dou, Qiwei Yao

Publication date: 6 September 2016

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1505.01177

zbMATH Keywords

stationarityhigh dimensionality\(\alpha\)-mixingleast squares estimationspatial autoregressiondynamic panels

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Applications of statistics to economics (62P20) Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

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Network Influence Analysis ⋮ Bootstrap joint prediction regions for sequences of missing values in spatio-temporal datasets ⋮ A flexible spatial autoregressive modelling framework for mixed covariates of multiple data types ⋮ Community network auto-regression for high-dimensional time series ⋮ Sparse generalized Yule-Walker estimation for large spatio-temporal autoregressions with an application to NO\(_2\) satellite data ⋮ Bipartite network influence analysis of a two-mode network ⋮ Banded spatio-temporal autoregressions ⋮ Portal nodes screening for large scale social networks ⋮ Reconstructing missing data sequences in multivariate time series: an application to environmental data ⋮ Sparse spatio-temporal autoregressions by profiling and bagging

Cites Work

• Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both \(n\) and \(T\) are large
• GMM estimation of spatial autoregressive models with unknown heteroskedasticity
• Estimation for spatial dynamic panel data with fixed effects: the case of spatial cointegration
• Some mixing properties of time series models
• Testing panel data regression models with spatial error correlation.
• Nonlinear time series. Nonparametric and parametric methods

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