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Finite sample properties of estimators of spatial autoregressive models with autoregressive disturbances
Author(s) -
Das Debabrata,
Kelejian Harry H.,
Prucha Ingmar R.
Publication year - 2003
Publication title -
papers in regional science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.937
H-Index - 64
eISSN - 1435-5957
pISSN - 1056-8190
DOI - 10.1111/j.1435-5597.2003.tb00001.x
Subject(s) - autoregressive model , estimator , mathematics , star model , sample (material) , statistics , econometrics , invariant estimator , minimax estimator , minimum variance unbiased estimator , autoregressive integrated moving average , time series , physics , thermodynamics
. The article investigates the finite sample properties of estimators for spatial autoregressive models where the disturbance terms may follow a spatial autoregressive process. In particular we investigate the finite sample behavior of the feasible generalized spatial two‐stage least squares (FGS2SLS) estimator introduced by Kelejian and Prucha (1998), the maximum likelihood (ML) estimator, as well as that of several other estimators. We find that the FGS2SLS estimator is virtually as efficient as the ML estimator. This is important because the ML estimator is computationally burdensome, and may even be forbidding in large samples, while the FGS2SLS estimator remains computationally feasible in large samples.