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A comparison of vector autoregressive forecasting performance: spatial versus non-spatial Bayesian priors

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  • James LeSage
  • Bryce Cashell

Abstract

Forecasting performance of spatial versus non-spatial Bayesian priors applied to a large vector autoregressive model that includes the 48 lower US states plus and the District of Columbia is explored. Accuracy of one- to six-quarter-ahead personal income forecasts is compared for a model based on the Minnesota prior used in macroeconomic forecasting and a spatial prior proposed by Krivelyova and LeSage (J Reg Sci 39(2):297–317, 1999 ). While the Minnesota prior emphasizes time dependence taking the form of a random walk, the spatial prior relies on past values of neighboring state income growth rates while ignoring own-state past income growth. Our findings indicate that forecast accuracy for longer future time horizons is improved by the spatial prior, while that for shorter horizons is better for the non-spatial prior. This motivated a hybrid approach that combines both spatial and time dependence in the prior restrictions placed on the model parameters. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • 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.
    • Handle: RePEc:spr:anresc:v:54:y:2015:i:2:p:533-560
    DOI: 10.1007/s00168-015-0665-1
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    References listed on IDEAS

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    6. 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.
    7. 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.
    8. Lesage, James P & Magura, Michael, 1990. "Using Bayesian Techniques for Data Pooling in Regional Payroll Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(1), pages 127-135, January.
    9. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    10. 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.
    11. Rickman, Dan S. & Miller, Steven R., 2002. "An Evaluation of Alternative Strategies for Incorporating Interindustry Relationships into a Regional Employment Forecasting Model," The Review of Regional Studies, Southern Regional Science Association, vol. 32(1), pages 133-147, Winter/Sp.
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    Cited by:

    1. Florian Eckert & Nina Mühlebach, 2021. "Global and Local Components of Output Gaps," KOF Working papers 21-497, KOF Swiss Economic Institute, ETH Zurich.
    2. Sergio J. Rey & Wei Kang & Levi Wolf, 2016. "The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," Journal of Geographical Systems, Springer, vol. 18(4), pages 377-398, October.
    3. 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.
    4. Wei Kang & Sergio J. Rey, 2018. "Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 73-93, July.
    5. Andrés-Rosales, Roldán & Quintana-Romero, Luis & de Jesús-Almonte, Leobardo & del Río-Rama, María de la Cruz, 2021. "Spatial spillovers of economic growth and public spending in Mexico: Evidence from a SpVAR model, 1999–2019," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 660-673.
    6. Florian Eckert & Nina Mühlebach, 2023. "Global and local components of output gaps," Empirical Economics, Springer, vol. 65(5), pages 2301-2331, November.
    7. 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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    More about this item

    Keywords

    C11; C22; R11;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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