GMM estimation of spatial autoregressive models with moving average disturbances

Osman Doǧan, Süleyman Taşpinar*

*Bu çalışma için yazışmadan sorumlu yazar

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16 Atıf (Scopus)

Özet

In this paper, we introduce the one-step generalized method of moments (GMM) estimation methods considered in Lee (2007a) and Liu, Lee, and Bollinger (2010) to spatial models that impose a spatial moving average process for the disturbance term. First, we determine the set of best linear and quadratic moment functions for GMM estimation. Second, we show that the optimal GMM estimator (GMME) formulated from this set is the most efficient estimator within the class of GMMEs formulated from the set of linear and quadratic moment functions. Our analytical results show that the one-step GMME can be more efficient than the quasi maximum likelihood (QMLE), when the disturbance term is simply i.i.d. With an extensive Monte Carlo study, we compare its finite sample properties against the MLE, the QMLE and the estimators suggested in Fingleton (2008a).

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)903-926
Sayfa sayısı24
DergiRegional Science and Urban Economics
Hacim43
Basın numarası6
DOI'lar
Yayın durumuYayınlandı - Kas 2013
Harici olarak yayınlandıEvet

Finansman

We are grateful to the editor, Daniel McMillen, and two anonymous referees for their helpful comments for the improvement of this study. We would like to thank Wim Vijverberg for his instructive comments on this study. Any remaining errors are our own. This research was supported, in part, by a grant of computer time from the City University of New York High Performance Computing Center under NSF grants CNS-0855217 and CNS-0958379 .

FinansörlerFinansör numarası
City University of New York High Performance Computing Center
National Science FoundationCNS-0958379, CNS-0855217

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