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Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models

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  • James P. LeSage

    (Department of Economics, University of Toledo, Toledo OH 43606 USA)

  • Zheng Pan

    (Department of Economics, University of Toledo, Toledo OH 43606 USA)

Abstract

Spatial contiguity relationships represent a frequently ignored source of information that is available to economists modeling cross-sections of metropolitan areas, counties, states, regions, and countries. Shown here is how contiguity relationships can be incorporated as prior information in Bayesian vector autoregressive and error correction models with little or no effort, using existing software, to produce improvements in forecasting performance. A comparison of alternative forecasting methods is undertaken using annual postwar time series of agricultural output for a sample of 15 corn-producing states. The models that incorporate prior information regarding spatial contiguity are found to dominate those that ignore this information, producing much better forecasts.

Suggested Citation

  • James P. LeSage & Zheng Pan, 1995. "Using Spatial Contiguity as Bayesian Prior Information in Regional Forecasting Models," International Regional Science Review, , vol. 18(1), pages 33-53, January.
    • Handle: RePEc:sae:inrsre:v:18:y:1995:i:1:p:33-53
    DOI: 10.1177/016001769501800102
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    Cited by:

    1. Xiaojie Xu, 2017. "Short-run price forecast performance of individual and composite models for 496 corn cash markets," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(14), pages 2593-2620, October.
    2. Rangan Gupta & Stephen Miller, 2012. "“Ripple effects” and forecasting home prices in Los Angeles, Las Vegas, and Phoenix," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 48(3), pages 763-782, June.
    3. Bokun, Kathryn O. & Jackson, Laura E. & Kliesen, Kevin L. & Owyang, Michael T., 2023. "FRED-SD: A real-time database for state-level data with forecasting applications," International Journal of Forecasting, Elsevier, vol. 39(1), pages 279-297.
    4. Xu Xiaojie, 2018. "Using Local Information to Improve Short-Run Corn Price Forecasts," Journal of Agricultural & Food Industrial Organization, De Gruyter, vol. 16(1), pages 1-15, January.
    5. Todd Kuethe & Valerien Pede, 2011. "Regional Housing Price Cycles: A Spatio-temporal Analysis Using US State-level Data," Regional Studies, Taylor & Francis Journals, vol. 45(5), pages 563-574.
    6. Valter Giacinto, 2010. "On vector autoregressive modeling in space and time," Journal of Geographical Systems, Springer, vol. 12(2), pages 125-154, June.
    7. Lambert, Dayton M. & Malzer, Gary L. & Lowenberg-DeBoer, James, 2004. "General Moment And Quasi-Maximum Likelihood Estimation Of A Spatially Autocorrelated System Of Equations: An Empirical Example Using On-Farm Precision Agriculture Data," Staff Papers 28667, Purdue University, Department of Agricultural Economics.
    8. Marfatia Hardik A., 2021. "Modeling House Price Synchronization across the U.S. States and their Time-Varying Macroeconomic Linkages," Journal of Time Series Econometrics, De Gruyter, vol. 13(1), pages 73-117, January.
    9. Rangan Gupta & Sonali Das, 2010. "Predicting Downturns in the US Housing Market: A Bayesian Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 41(3), pages 294-319, October.
    10. Valter Di Giacinto, 2003. "Differential Regional Effects of Monetary Policy: A Geographical SVAR Approach," International Regional Science Review, , vol. 26(3), pages 313-341, July.
    11. Rangan Gupta & Sonali Das, 2008. "Spatial Bayesian Methods Of Forecasting House Prices In Six Metropolitan Areas Of South Africa," South African Journal of Economics, Economic Society of South Africa, vol. 76(2), pages 298-313, June.
    12. Rangan Gupta & Alain Kabundi & Stephen M. Miller, 2009. "Using Large Data Sets to Forecast Housing Prices: A Case Study of Twenty US States," Working papers 2009-13, University of Connecticut, Department of Economics.
    13. Alain Kabundi & Rangan Gupta & Sonali Das, 2008. "Is a DFM well suited for forecasting regional house price inflation?," Working Papers 085, Economic Research Southern Africa.
    14. James LeSage & Bryce Cashell, 2015. "A comparison of vector autoregressive forecasting performance: spatial versus non-spatial Bayesian priors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 54(2), pages 533-560, March.
    15. Rangan Gupta & Stephen Miller, 2012. "The Time-Series Properties of House Prices: A Case Study of the Southern California Market," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 339-361, April.
    16. Miguel A. Márquez & Julián Ramajo & Geoffrey JD. Hewings, 2015. "Regional growth and spatial spillovers: Evidence from an SpVAR for the Spanish regions," Papers in Regional Science, Wiley Blackwell, vol. 94, pages 1-18, November.
    17. Dowd, Michael R. & LeSage, James P., 1997. "Analysis of spatial contiguity influences on state price level formation," International Journal of Forecasting, Elsevier, vol. 13(2), pages 245-253, June.
    18. Susi Gorbey & Doug James & Jacques Poot, 1999. "Population Forecasting with Endogenous Migration: An Application to Trans-Tasman Migration," International Regional Science Review, , vol. 22(1), pages 69-101, April.
    19. Marco Percoco, 2007. "Evaluating forecasting accuracy of the temporally aggregated space-time autoregressive model," Applied Economics Letters, Taylor & Francis Journals, vol. 14(9), pages 637-641.
    20. Rangan Gupta, 2009. "Bayesian Methods Of Forecasting Inventory Investment," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 113-126, March.
    21. Víctor Hugo Torres Preciado, 2017. "Desempleo y criminalidad en los estados de la frontera norte de México: un enfoque espacial bayesiano de vectores auto-regresivos. (Unemployment and crime in the Northern-border states of Mexico: a sp," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 25-58, May.
    22. Dan S. Rickman & Steven R. Miller & Russell McKenzie, 2009. "Spatial and sectoral linkages in regional models: A Bayesian vector autoregression forecast evaluation," Papers in Regional Science, Wiley Blackwell, vol. 88(1), pages 29-41, March.
    23. James P. LeSage & Daniel Hendrikz, 2019. "Large Bayesian vector autoregressive forecasting for regions: A comparison of methods based on alternative disturbance structures," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 62(3), pages 563-599, June.

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