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Computational aspects of the EM algorithm for spatial econometric models with missing data

Journal Article


Abstract


  • Maximum likelihood (ML) estimation with spatial econometric models is a long-standing problem that finds application in several areas of economic importance. The problem is particularly challenging in the presence of missing data, since there is an implied dependence between all units, irrespective of whether they are observed or not. Out of the several approaches adopted for ML estimation in this context, that of LeSage and Pace [Models for spatially dependent missing data. J Real Estate Financ Econ. 2004;29(2):233¿254] stands out as one of the most commonly used with spatial econometric models due to its ability to scale with the number of units. Here, we review their algorithm, and consider several similar alternatives that are also suitable for large datasets. We compare the methods through an extensive empirical study and conclude that, while the approximate approaches are suitable for large sampling ratios, for small sampling ratios the only reliable algorithms are those that yield exact ML or restricted ML estimates.

Publication Date


  • 2017

Citation


  • Suesse, T. & Zammit-Mangion, A. (2017). Computational aspects of the EM algorithm for spatial econometric models with missing data. Journal of Statistical Computation and Simulation, Online First 1-20.

Scopus Eid


  • 2-s2.0-85013129719

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6544

Has Global Citation Frequency


Number Of Pages


  • 19

Start Page


  • 1

End Page


  • 20

Volume


  • Online First

Place Of Publication


  • United Kingdom

Abstract


  • Maximum likelihood (ML) estimation with spatial econometric models is a long-standing problem that finds application in several areas of economic importance. The problem is particularly challenging in the presence of missing data, since there is an implied dependence between all units, irrespective of whether they are observed or not. Out of the several approaches adopted for ML estimation in this context, that of LeSage and Pace [Models for spatially dependent missing data. J Real Estate Financ Econ. 2004;29(2):233¿254] stands out as one of the most commonly used with spatial econometric models due to its ability to scale with the number of units. Here, we review their algorithm, and consider several similar alternatives that are also suitable for large datasets. We compare the methods through an extensive empirical study and conclude that, while the approximate approaches are suitable for large sampling ratios, for small sampling ratios the only reliable algorithms are those that yield exact ML or restricted ML estimates.

Publication Date


  • 2017

Citation


  • Suesse, T. & Zammit-Mangion, A. (2017). Computational aspects of the EM algorithm for spatial econometric models with missing data. Journal of Statistical Computation and Simulation, Online First 1-20.

Scopus Eid


  • 2-s2.0-85013129719

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/6544

Has Global Citation Frequency


Number Of Pages


  • 19

Start Page


  • 1

End Page


  • 20

Volume


  • Online First

Place Of Publication


  • United Kingdom