Heteroskedasticity-consistent covariance matrix estimators for spatial autoregressive models

Süleyman Taşpınar*, Osman Doğan, Anil K. Bera

*Bu çalışma için yazışmadan sorumlu yazar

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12 Atıf (Scopus)

Özet

In the presence of heteroskedasticity, conventional test statistics based on the ordinary least squares (OLS) estimator lead to incorrect inference results for the linear regression model. Given that heteroskedasticity is common in cross-sectional data, the test statistics based on various forms of heteroskedasticity-consistent covariance matrices (HCCMs) have been developed in the literature. In contrast to the standard linear regression model, heteroskedasticity is a more serious problem for spatial econometric models, generally causing inconsistent extremum estimators of model coefficients. This paper investigates the finite sample properties of the heteroskedasticity-robust generalized method of moments estimator (RGMME) for a spatial econometric model with an unknown form of heteroskedasticity. In particular, it develops various HCCM-type corrections to improve the finite sample properties of the RGMME and the conventional Wald test. The Monte Carlo results indicate that the HCCM-type corrections can produce more accurate results for inference on model parameters and the impact effects estimates in small samples.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)241-268
Sayfa sayısı28
DergiSpatial Economic Analysis
Hacim14
Basın numarası2
DOI'lar
Yayın durumuYayınlandı - 3 Nis 2019
Harici olarak yayınlandıEvet

Bibliyografik not

Publisher Copyright:
© 2018, © 2018 Regional Studies Association.

Finansman

The CUNY HPCC is operated by the College of Staten Island and funded, in part, by grants from the City of New York, State of New York, CUNY Research Foundation, and National Science Foundation [grant numbers CNS-0958379, CNS-0855217 and ACI 1126113.

FinansörlerFinansör numarası
College of Staten Island
National Science FoundationCNS-0958379, CNS-0855217, ACI 1126113
City University of New York
City College of New York

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