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Software for Bayesian cross section and panel spatial model comparison

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Abstract

A wide variety of spatial regression specifications that include alternative types of spatial dependence (e.g., lagged values of the dependent variable, spatial lags of explanatory variables, dependence in the model disturbances) have been the focus of a literature on statistical tests for distinguishing between alternative specifications. LeSage (Spat Stat 9:122–145, 2014) argues that a Bayesian approach to model comparison for cross-sectional and static panel models considerably simplifies the task of selecting an appropriate model. MATLAB software functions for carrying out Bayesian cross-sectional and static spatial panel model comparisons described in LeSage (2014) are described here along with a number of illustrative applications.

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Notes

  1. All references to the toolbox are to the Spatial Econometrics Toolbox for MATLAB available for free at www.spatial-econometrics.com.

  2. Users can rely on any structure variable name, for example, options. lflag = 0 would work, with the name ‘options’ entered in place of ‘info’.

  3. When using the default MC determinant option, info.lflag = 1, every run of the program will produce slightly different results because a Monte Carlo approximation to the log-determinant calculation is being used (see Barry and Pace 1999).

  4. The SLX model results are identical because no numerical integration is required to calculate the log-marginal likelihood in this case. Varying degrees of accuracy in numerical integration are what explains the difference in results (see LeSage 2014 for details regarding this).

  5. LeSage (2014) shows that these three models subsume other models such as SAR and SEM as special cases, following the same logic as set forth in LeSage and Pace (2009).

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Authors and Affiliations

  1. Department of Finance and Economics, Texas State University, San Marcos, TX, 78666, USA

    James P. LeSage

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  1. James P. LeSage
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Correspondence to James P. LeSage.

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LeSage, J.P. Software for Bayesian cross section and panel spatial model comparison. J Geogr Syst 17, 297–310 (2015). https://doi.org/10.1007/s10109-015-0217-3

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  • DOI: https://doi.org/10.1007/s10109-015-0217-3

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