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Efficiency of the OLS estimator in the vicinity of a spatial unit root

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  • Martellosio, Federico

Abstract

Previous results have indicated that the OLS estimator of the vector of regression coefficients can be nearly as efficient as the best linear unbiased estimator when the regression errors follow a spatial process with root in the vicinity of unity. Such results were derived under the assumption of a symmetric weights matrix, which simplifies the analysis considerably, but is very often not satisfied in applications. This paper provides nontrivial generalizations to the important case of nonsymmetric weights matrices.

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  • Martellosio, Federico, 2011. "Efficiency of the OLS estimator in the vicinity of a spatial unit root," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1285-1291, August.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:8:p:1285-1291
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    References listed on IDEAS

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    1. Lung-Fei Lee & Jihai Yu, 2009. "Spatial Nonstationarity and Spurious Regression: the Case with a Row-normalized Spatial Weights Matrix," Spatial Economic Analysis, Taylor & Francis Journals, vol. 4(3), pages 301-327.
    2. Kleiber, Christian, 2001. "Finite sample efficiency of OLS in linear regression models with long-memory disturbances," Economics Letters, Elsevier, vol. 72(2), pages 131-136, August.
    3. Baltagi, Badi H. & Liu, Long, 2010. "Spurious spatial regression with equal weights," Statistics & Probability Letters, Elsevier, vol. 80(21-22), pages 1640-1642, November.
    4. Liu, Xiaodong & Lee, Lung-fei, 2010. "GMM estimation of social interaction models with centrality," Journal of Econometrics, Elsevier, vol. 159(1), pages 99-115, November.
    5. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
    6. Kramer, Walter & Baltagi, Badi, 1996. "A general condition for an optimal limiting efficiency of OLS in the general linear regression model," Economics Letters, Elsevier, vol. 50(1), pages 13-17, January.
    7. Harry H. Kelejian & Ingmar R. Prucha & Yevgeny Yuzefovich, 2006. "Estimation Problems In Models With Spatial Weighting Matrices Which Have Blocks Of Equal Elements," Journal of Regional Science, Wiley Blackwell, vol. 46(3), pages 507-515, August.
    8. Martellosio, Federico, 2010. "Power Properties Of Invariant Tests For Spatial Autocorrelation In Linear Regression," Econometric Theory, Cambridge University Press, vol. 26(1), pages 152-186, February.
    9. Roland Jeske & Seuck Song, 2003. "Relative efficiency of OLSE and COTE for seasonal autoregressive disturbances," Statistical Papers, Springer, vol. 44(3), pages 421-432, July.
    10. Baran, Sándor & Pap, Gyula & van Zuijlen, Martien C. A., 2004. "Asymptotic inference for a nearly unstable sequence of stationary spatial AR models," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 53-61, August.
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    Cited by:

    1. Grant Hillier & Federico Martellosio, 2013. "Properties of the maximum likelihood estimator in spatial autoregressive models," CeMMAP working papers 44/13, Institute for Fiscal Studies.
    2. Preinerstorfer, David & Pötscher, Benedikt M., 2017. "On The Power Of Invariant Tests For Hypotheses On A Covariance Matrix," Econometric Theory, Cambridge University Press, vol. 33(1), pages 1-68, February.
    3. Grant Hillier & Federico Martellosio, 2013. "Properties of the maximum likelihood estimator in spatial autoregressive models," CeMMAP working papers CWP44/13, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    4. Badi H. Baltagi & Chihwa Kao & Long Liu, 2013. "The Estimation and Testing of a Linear Regression with Near Unit Root in the Spatial Autoregressive Error Term," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(3), pages 241-270, September.
    5. Patalee, M.A. Buddhika & Tonsor, Glynn T., 2021. "Impact of weather on cow-calf industry locations and production in the United States," Agricultural Systems, Elsevier, vol. 193(C).

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