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

Authors

Thomas F. Suesse, University of WollongongFollow
Andrew Zammit-Mangion, University of WollongongFollow

RIS ID

112579

Publication Details

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.

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.