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Locally most powerful tests for spatial interactions in the simultaneous SAR Tobit model

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  • Qu, Xi
  • Lee, Lung-fei

Abstract

The simultaneous SAR Tobit model is useful to analyze censored data in a spatial or social interaction setting. This paper focuses on three classical tests of spatial interactions in the simultaneous SAR Tobit model. We derive the asymptotic distributions of those three tests under the null and the local alternative hypotheses, establish their asymptotic equivalence and local efficiency, and study finite sample properties by the Monte Carlo simulation. The tests are applied to an empirical example on the presence of competition among school districts on school district income tax in Iowa.

Suggested Citation

  • Qu, Xi & Lee, Lung-fei, 2013. "Locally most powerful tests for spatial interactions in the simultaneous SAR Tobit model," Regional Science and Urban Economics, Elsevier, vol. 43(2), pages 307-321.
    • Handle: RePEc:eee:regeco:v:43:y:2013:i:2:p:307-321
    DOI: 10.1016/j.regsciurbeco.2012.07.010
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    References listed on IDEAS

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    1. Qu, Xi & Lee, Lung-fei, 2012. "LM tests for spatial correlation in spatial models with limited dependent variables," Regional Science and Urban Economics, Elsevier, vol. 42(3), pages 430-445.
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    Citations

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    Cited by:

    1. Anna Gloria Billé & Samantha Leorato, 2017. "Quasi-ML estimation, Marginal Effects and Asymptotics for Spatial Autoregressive Nonlinear Models," BEMPS - Bozen Economics & Management Paper Series BEMPS44, Faculty of Economics and Management at the Free University of Bozen.
    2. Cheng, Wei & Lee, Lung-fei, 2017. "Testing endogeneity of spatial and social networks," Regional Science and Urban Economics, Elsevier, vol. 64(C), pages 81-97.
    3. Ievoli Corrado & Basile Roberto Giovanni & Belliggiano Angelo, 2017. "The Spatial Patterns of Dairy Farming In Molise," European Countryside, Sciendo, vol. 9(4), pages 729-745, December.
    4. Yang, Chao & Lee, Lung-fei & Qu, Xi, 2018. "Tobit models with social interactions: Complete vs incomplete information," Regional Science and Urban Economics, Elsevier, vol. 73(C), pages 30-50.
    5. Bera Anil K. & Doğan Osman & Taşpınar Süleyman, 2019. "Testing Spatial Dependence in Spatial Models with Endogenous Weights Matrices," Journal of Econometric Methods, De Gruyter, vol. 8(1), pages 1-33, January.
    6. Ye, Xiaoqing & Xu, Juan & Wu, Xiangjun, 2018. "Estimation of an unbalanced panel data Tobit model with interactive effects," Journal of choice modelling, Elsevier, vol. 28(C), pages 108-123.
    7. Kim, Changjoo & Parent, Olivier, 2016. "Modeling individual travel behaviors based on intra-household interactions," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 1-11.
    8. Xu, Xingbai & Lee, Lung-fei, 2018. "Sieve maximum likelihood estimation of the spatial autoregressive Tobit model," Journal of Econometrics, Elsevier, vol. 203(1), pages 96-112.
    9. Helen Naughton & Pehr-Johan Norbäck & Ayça Tekin-Koru, 2016. "Aggregation Issues of Foreign Direct Investment Estimation in an Interdependent World," The World Economy, Wiley Blackwell, vol. 39(12), pages 2046-2073, December.
    10. Xu, Xingbai & Lee, Lung-fei, 2015. "Maximum likelihood estimation of a spatial autoregressive Tobit model," Journal of Econometrics, Elsevier, vol. 188(1), pages 264-280.
    11. Wan, Qunchao & Chen, Jin & Yao, Zhu & Yuan, Ling, 2022. "Preferential tax policy and R&D personnel flow for technological innovation efficiency of China's high-tech industry in an emerging economy," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    12. Wei Cheng, 2022. "Consistent EM algorithm for a spatial autoregressive probit model," Journal of Spatial Econometrics, Springer, vol. 3(1), pages 1-23, December.
    13. Jin, Fei & Lee, Lung-fei, 2019. "GEL estimation and tests of spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 208(2), pages 585-612.
    14. Xu, Xingbai & Lee, Lung-fei, 2015. "A spatial autoregressive model with a nonlinear transformation of the dependent variable," Journal of Econometrics, Elsevier, vol. 186(1), pages 1-18.
    15. Hsieh, Chih-Sheng & Lin, Xu, 2017. "Gender and racial peer effects with endogenous network formation," Regional Science and Urban Economics, Elsevier, vol. 67(C), pages 135-147.

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    More about this item

    Keywords

    Spatial Tobit model; Three classical tests; Locally most powerful;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • R50 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Regional Government Analysis - - - General

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