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Cox-type tests for competing spatial autoregressive models with spatial autoregressive disturbances

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  • Jin, Fei
  • Lee, Lung-fei

Abstract

In this paper, we consider the Cox-type tests of non-nested hypotheses for spatial autoregressive (SAR) models with SAR disturbances. We formally derive the asymptotic distributions of the test statistics. In contrast to regression models, we show that the Cox-type and J-type tests for non-nested hypotheses in the framework of SAR models are not asymptotically equivalent under the null hypothesis. The Cox test in a non-spatial setting has been found often to have large size distortion, which can be removed by bootstrap. Cox-type tests for SAR models with SAR disturbances may also have a large size distortion. We show that the bootstrap is consistent for Cox-type tests in our framework. Performances of the Cox-type and J-type tests as well as their bootstrapped versions in finite samples are compared via a Monte Carlo study. These tests are of particular interest when there are competing models with different spatial weight matrices. Using bootstrapped p-values, the Cox tests have relatively high power in all experiments and can outperform J-type and several other related tests in some cases.

Suggested Citation

  • Jin, Fei & Lee, Lung-fei, 2013. "Cox-type tests for competing spatial autoregressive models with spatial autoregressive disturbances," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 590-616.
  • Handle: RePEc:eee:regeco:v:43:y:2013:i:4:p:590-616
    DOI: 10.1016/j.regsciurbeco.2013.03.003
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    Citations

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    Cited by:

    1. Harry H. Kelejian & Gianfranco Piras, 2013. "A J-Test for Panel Models with Fixed Effects, Spatial and Time," Working Papers Working Paper 2013-03, Regional Research Institute, West Virginia University.
    2. repec:eee:regeco:v:69:y:2018:i:c:p:48-68 is not listed on IDEAS
    3. Shew Fan Liu & Zhenlin Yang, 2015. "Asymptotic Distribution and Finite Sample Bias Correction of QML Estimators for Spatial Error Dependence Model," Econometrics, MDPI, Open Access Journal, vol. 3(2), pages 1-36, May.
    4. repec:oup:jfinec:v:15:y:2017:i:2:p:286-301. is not listed on IDEAS
    5. Jin, Fei & Lee, Lung-fei, 2015. "On the bootstrap for Moran’s I test for spatial dependence," Journal of Econometrics, Elsevier, vol. 184(2), pages 295-314.
    6. Nicolas Debarsy & Cem Ertur, 2016. "Interaction matrix selection in spatial econometrics with an application to growth theory," Working Papers halshs-01278545, HAL.
    7. Delgado, Miguel A. & Robinson, Peter, 2015. "Non-nested testing of spatial correlation," LSE Research Online Documents on Economics 61433, London School of Economics and Political Science, LSE Library.
    8. Delgado, Miguel A. & Robinson, Peter M., 2015. "Non-nested testing of spatial correlation," Journal of Econometrics, Elsevier, vol. 187(1), pages 385-401.
    9. Liu, Shew Fan & Yang, Zhenlin, 2015. "Improved inferences for spatial regression models," Regional Science and Urban Economics, Elsevier, vol. 55(C), pages 55-67.

    More about this item

    Keywords

    Specification; Spatial autoregressive model; Non-nested; Cox test; J test; QMLE;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • R15 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Econometric and Input-Output Models; Other Methods

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