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Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting

Author

Listed:
  • George Duncan

    (Heinz School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Wilpen Gorr

    (Heinz School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Janusz Szczypula

    (Heinz School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

One important implementation of Bayesian forecasting is the Multi-State Kalman Filter (MSKF) method. It is particularly suited for short and irregular time series data. In certain applications, time series data are available on numerous parallel observational units which, while not having cause-and-effect relationships between them, are subject to the same external forces (e.g., business cycles). Treating them separately may lose useful information for forecasting. For such situations, involving seemingly unrelated time series, this article develops a Bayesian forecasting method called C-MSKF that combines the MSKF method with the Conditionally Independent Hierarchical method. A case study on forecasting income tax revenue for each of forty school districts in Allegheny County, Pennsylvania, based on fifteen years of data, is used to illustrate the application of C-MSKF in comparison with univariate MSKF. Results show that C-MSKF is more accurate than MSKF. The relative accuracy of C-MSKF increases with decreasing length of historical time series data, increasing forecasting horizon, and sensitivity of school districts to the economic cycle.

Suggested Citation

  • George Duncan & Wilpen Gorr & Janusz Szczypula, 1993. "Bayesian Forecasting for Seemingly Unrelated Time Series: Application to Local Government Revenue Forecasting," Management Science, INFORMS, vol. 39(3), pages 275-293, March.
    • Handle: RePEc:inm:ormnsc:v:39:y:1993:i:3:p:275-293
    DOI: 10.1287/mnsc.39.3.275
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    Cited by:

    1. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    2. Shoesmith, Gary L., 2013. "Space–time autoregressive models and forecasting national, regional and state crime rates," International Journal of Forecasting, Elsevier, vol. 29(1), pages 191-201.
    3. 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.
    4. Chen, Huijing & Boylan, John E., 2008. "Empirical evidence on individual, group and shrinkage seasonal indices," International Journal of Forecasting, Elsevier, vol. 24(3), pages 525-534.
    5. Lu, Emiao & Handl, Julia & Xu, Dong-ling, 2018. "Determining analogies based on the integration of multiple information sources," International Journal of Forecasting, Elsevier, vol. 34(3), pages 507-528.
    6. JS Armstrong & Fred Collopy, 2004. "Integration of Statistical Methods and Judgment for Time Series," General Economics and Teaching 0412024, University Library of Munich, Germany.
    7. H Chen & J E Boylan, 2007. "Use of individual and group seasonal indices in subaggregate demand forecasting," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(12), pages 1660-1671, December.
    8. Gorr, Wilpen & Olligschlaeger, Andreas & Thompson, Yvonne, 2003. "Short-term forecasting of crime," International Journal of Forecasting, Elsevier, vol. 19(4), pages 579-594.

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