GMM estimation of spatial autoregressive models with moving average disturbances

Osman Doǧan; Süleyman Taşpinar

doi:10.1016/j.regsciurbeco.2013.09.002

GMM estimation of spatial autoregressive models with moving average disturbances

Osman Doǧan
, Süleyman Taşpinar^*

^*Bu çalışma için yazışmadan sorumlu yazar

City University of New York

Araştırma çıktısı: Dergi yayını › Makale › Hakem

18 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 dil	İngilizce
Sayfa (başlangıç-bitiş)	903-926
Sayfa sayısı	24
Dergi	Regional Science and Urban Economics
Hacim	43
Basın numarası	6
DOI'lar	https://doi.org/10.1016/j.regsciurbeco.2013.09.002
Yayın durumu	Yayınlandı - Kas 2013
Harici olarak yayınlandı	Evet

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 .

Finansörler	Finansör numarası
City University of New York High Performance Computing Center
National Science Foundation	CNS-0958379, CNS-0855217

Belgeye Erişim

10.1016/j.regsciurbeco.2013.09.002

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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title = "GMM estimation of spatial autoregressive models with moving average disturbances",

abstract = "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).",

keywords = "Asymptotics, GMM, SARMA, SMA, Spatial autocorrelation, Spatial dependence, Spatial moving average process",

author = "Osman Doǧan and S{\"u}leyman Ta{\c s}pinar",

year = "2013",

month = nov,

doi = "10.1016/j.regsciurbeco.2013.09.002",

language = "English",

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journal = "Regional Science and Urban Economics",

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T1 - GMM estimation of spatial autoregressive models with moving average disturbances

AU - Doǧan, Osman

AU - Taşpinar, Süleyman

PY - 2013/11

Y1 - 2013/11

N2 - 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).

AB - 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).

KW - Asymptotics

KW - GMM

KW - SARMA

KW - SMA

KW - Spatial autocorrelation

KW - Spatial dependence

KW - Spatial moving average process

UR - https://www.scopus.com/pages/publications/84885081712

U2 - 10.1016/j.regsciurbeco.2013.09.002

DO - 10.1016/j.regsciurbeco.2013.09.002

M3 - Article

AN - SCOPUS:84885081712

SN - 0166-0462

VL - 43

SP - 903

EP - 926

JO - Regional Science and Urban Economics

JF - Regional Science and Urban Economics

IS - 6

ER -

GMM estimation of spatial autoregressive models with moving average disturbances

Özet

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

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