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Bayesian estimation and model selection of threshold spatial Durbin model

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  • Zhu, Yanli
  • Han, Xiaoyi
  • Chen, Ying

Abstract

We consider a threshold spatial Durbin model that allows for threshold effects in both endogenous and exogenous spatial interactions among cross-sectional units. We develop a computationally tractable Markov Chain Monte Carlo (MCMC) algorithm to estimate the model. We also propose a nested model selection procedure to test for spatial threshold effects, based upon the Bayes factor computed from the Savage–Dickey Density Ratio in Verdinelli and Wasserman (1995). Simulation studies suggest that the Bayesian estimator is more precise than the spatial 2SLS (S2SLS) estimator in Deng (2018). The model selection procedure works well when the sample size increases and the difference between spatial parameters enlarges.

Suggested Citation

  • Zhu, Yanli & Han, Xiaoyi & Chen, Ying, 2020. "Bayesian estimation and model selection of threshold spatial Durbin model," Economics Letters, Elsevier, vol. 188(C).
  • Handle: RePEc:eee:ecolet:v:188:y:2020:i:c:s0165176520300094
    DOI: 10.1016/j.econlet.2020.108956
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    1. Koop, Gary & Potter, Simon M, 1999. "Dynamic Asymmetries in U.S. Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(3), pages 298-312, July.
    2. Caner, Mehmet & Hansen, Bruce E., 2004. "Instrumental Variable Estimation Of A Threshold Model," Econometric Theory, Cambridge University Press, vol. 20(5), pages 813-843, October.
    3. Victor Lavy & Analia Schlosser, 2011. "Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(2), pages 1-33, April.
    4. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    5. Deng, Ying, 2018. "Estimation for the spatial autoregressive threshold model," Economics Letters, Elsevier, vol. 171(C), pages 172-175.
    6. Mithat Gönen & Wesley O. Johnson & Yonggang Lu & Peter H. Westfall, 2019. "Comparing Objective and Subjective Bayes Factors for the Two-Sample Comparison: The Classification Theorem in Action," The American Statistician, Taylor & Francis Journals, vol. 73(1), pages 22-31, January.
    7. Lee, Lung-fei, 2007. "The method of elimination and substitution in the GMM estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 140(1), pages 155-189, September.
    8. Bruce E. Hansen, 2000. "Sample Splitting and Threshold Estimation," Econometrica, Econometric Society, vol. 68(3), pages 575-604, May.
    9. Chih‐Sheng Hsieh & Hans van Kippersluis, 2018. "Smoking initiation: Peers and personality," Quantitative Economics, Econometric Society, vol. 9(2), pages 825-863, July.
    10. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    11. Min Wang & Guangying Liu, 2016. "A Simple Two-Sample Bayesian t -Test for Hypothesis Testing," The American Statistician, Taylor & Francis Journals, vol. 70(2), pages 195-201, May.
    12. Gonen, Mithat & Johnson, Wesley O. & Lu, Yonggang & Westfall, Peter H., 2005. "The Bayesian Two-Sample t Test," The American Statistician, American Statistical Association, vol. 59, pages 252-257, August.
    13. Fangwen Lu & Michael L. Anderson, 2015. "Peer Effects in Microenvironments: The Benefits of Homogeneous Classroom Groups," Journal of Labor Economics, University of Chicago Press, vol. 33(1), pages 91-122.
    14. John Geweke & Nobuhiko Terui, 1993. "Bayesian Threshold Autoregressive Models For Nonlinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 14(5), pages 441-454, September.
    15. Lu, Fangwen & Anderson, Michael L, 2015. "Peer Effects in Microenvironments: The Benefits of Homogeneous Classroom Groups," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt0872n398, Department of Agricultural & Resource Economics, UC Berkeley.
    16. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    17. Victor Lavy & Analía Schlosser, 2011. "Corrigendum: Mechanisms and Impacts of Gender Peer Effects at School," American Economic Journal: Applied Economics, American Economic Association, vol. 3(3), pages 268-268, July.
    18. Yu, Jihai & Zhou, Li-An & Zhu, Guozhong, 2016. "Strategic interaction in political competition: Evidence from spatial effects across Chinese cities," Regional Science and Urban Economics, Elsevier, vol. 57(C), pages 23-37.
    19. Lee, Lung-fei, 2007. "GMM and 2SLS estimation of mixed regressive, spatial autoregressive models," Journal of Econometrics, Elsevier, vol. 137(2), pages 489-514, April.
    20. Chen, Rong & Guo, Re-Jin & Lin, Ming, 2010. "Self-Selectivity in Firm’s Decision to Withdraw IPO: Bayesian Inference for Hazard Models of Bankruptcy With Feedback," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1297-1309.
    21. Lung-fei Lee & Xiaodong Liu & Xu Lin, 2010. "Specification and estimation of social interaction models with network structures," Econometrics Journal, Royal Economic Society, vol. 13(2), pages 145-176, July.
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    Cited by:

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    More about this item

    Keywords

    Threshold spatial Durbin model; Bayesian estimation; Bayes factor; Savage–Dickey density ratio;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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