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Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach

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  • Seya, Hajime
  • Yamagata, Yoshiki
  • Tsutsumi, Morito

Abstract

The recent progress of spatial econometrics has developed a new technique called the “spatial hedonic approach,” which considers the elements of spatial autocorrelation among property values and geographically distributed attributes. The practical difficulties in applying spatial econometric models include the specification of the spatial weight matrix (SWM), which affects the final analysis results. Some simulation studies suggest that information criteria such as AIC are useful for the SWM's selection, but if many model candidates exist (e.g., when the selections of explanatory variables are performed simultaneously), then the computational burden of calculating such criteria for each model is large. The present study develops an automatic model selection algorithm using the technique of reversible jump MCMC combined with simulated annealing; termed trans-dimensional simulated annealing (TDSA). The performance of the TDSA algorithm is verified using the well-known Boston housing dataset, and it is applied empirically to a Japanese real estate dataset. The obtained results suggest a two-step strategy for model selection, with SWM (W) first, followed by the explanatory variables (X and WX), will result in local optima, and therefore these variables should be selected simultaneously. The TDSA algorithm can find the significant variables that are “hidden” because of multicollinearity in the unrestricted model, and can attain the minimum AIC automatically.

Suggested Citation

  • Seya, Hajime & Yamagata, Yoshiki & Tsutsumi, Morito, 2013. "Automatic selection of a spatial weight matrix in spatial econometrics: Application to a spatial hedonic approach," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 429-444.
  • Handle: RePEc:eee:regeco:v:43:y:2013:i:3:p:429-444
    DOI: 10.1016/j.regsciurbeco.2013.02.002
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    Cited by:

    1. Thanos, Sotirios & Dubé, Jean & Legros, Diègo, 2016. "Putting time into space: the temporal coherence of spatial applications in the housing market," Regional Science and Urban Economics, Elsevier, vol. 58(C), pages 78-88.
    2. repec:spr:lsprsc:v:11:y:2018:i:1:d:10.1007_s12076-017-0199-5 is not listed on IDEAS
    3. repec:eee:regeco:v:68:y:2018:i:c:p:36-45 is not listed on IDEAS
    4. repec:taf:ijspmg:v:21:y:2017:i:3:p:240-255 is not listed on IDEAS
    5. Gong, Pu & Weng, Yingliang, 2016. "Value-at-Risk forecasts by a spatiotemporal model in Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 173-191.

    More about this item

    Keywords

    Spatial econometrics; Spatial weight matrix; Spatial lag model; Spatial Durbin model; Trans-dimensional simulated annealing;

    JEL classification:

    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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