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Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach

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  • Han, Xiaoyi
  • Hsieh, Chih-Sheng
  • Lee, Lung-fei

Abstract

In this paper we consider estimation and model selection of higher-order spatial autoregressive model by an efficient Bayesian approach. Based upon the exchange algorithm, we develop an efficient MCMC sampler, which does not rely on special features of spatial weights matrices and does not require the evaluation of the Jacobian determinant in the likelihood function. We also propose a computationally simple procedure to tackle nested model selection issues of higher-order spatial autoregressive models. We find that the exchange algorithm can be utilized to simplify the computation of Bayes factor through the Savage-Dickey density ratio. We apply the efficient estimation algorithm and the model selection procedure to study the “tournament competition” across Chinese cities and the spatial dependence of county-level voter participation rates in the 1980 U.S. presidential election.

Suggested Citation

  • Han, Xiaoyi & Hsieh, Chih-Sheng & Lee, Lung-fei, 2017. "Estimation and model selection of higher-order spatial autoregressive model: An efficient Bayesian approach," Regional Science and Urban Economics, Elsevier, vol. 63(C), pages 97-120.
    • Handle: RePEc:eee:regeco:v:63:y:2017:i:c:p:97-120
    DOI: 10.1016/j.regsciurbeco.2016.12.003
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    Cited by:

    1. Jiaxuan Liang & Yi Cheng & Yuqi Su & Shuyue Xiao & Yunquan Song, 2022. "Variable Selection for Spatial Logistic Autoregressive Models," Mathematics, MDPI, vol. 10(17), pages 1-16, August.
    2. Taşpınar, Süleyman & Doğan, Osman & Bera, Anil K., 2017. "GMM gradient tests for spatial dynamic panel data models," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 65-88.
    3. Cai, Zhengzheng & Zhu, Yanli & Han, Xiaoyi, 2022. "Bayesian analysis of spatial dynamic panel data model with convex combinations of different spatial weight matrices: A reparameterized approach," Economics Letters, Elsevier, vol. 217(C).
    4. James Paul LeSage, 2020. "Fast MCMC estimation of multiple W-matrix spatial regression models and Metropolis–Hastings Monte Carlo log-marginal likelihoods," Journal of Geographical Systems, Springer, vol. 22(1), pages 47-75, January.
    5. Badi H. Baltagi & Peter H. Egger & Michaela Kesina, 2022. "Bayesian estimation of multivariate panel probits with higher‐order network interdependence and an application to firms' global market participation in Guangdong," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1356-1378, November.
    6. Gupta, Abhimanyu, 2023. "Efficient closed-form estimation of large spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 148-167.
    7. Ye Yang & Osman Doğan & Süleyman Taşpınar, 2023. "Observed-data DIC for spatial panel data models," Empirical Economics, Springer, vol. 64(3), pages 1281-1314, March.

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    More about this item

    Keywords

    Higher-order spatial autoregressive model; Exchange algorithm; Bayesian estimation; Bayes factor; Savage-Dickey density ratio;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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