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A Bayesian Change Point Model for Historical Time Series Analysis

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  • Western, Bruce
  • Kleykamp, Meredith

Abstract

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

Suggested Citation

  • Western, Bruce & Kleykamp, Meredith, 2004. "A Bayesian Change Point Model for Historical Time Series Analysis," Political Analysis, Cambridge University Press, vol. 12(4), pages 354-374.
    • Handle: RePEc:cup:polals:v:12:y:2004:i:04:p:354-374_00
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    Cited by:

    1. Eric Ruggieri, 2018. "A pruned recursive solution to the multiple change point problem," Computational Statistics, Springer, vol. 33(2), pages 1017-1045, June.
    2. Céline Cunen & Nils Lid Hjort & Håvard Mokleiv Nygård, 2020. "Statistical sightings of better angels: Analysing the distribution of battle-deaths in interstate conflict over time," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(2), pages 221-234, March.
    3. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
    4. Gebrenegus Ghilagaber & Parfait Munezero, 2020. "Bayesian change-point modelling of the effects of 3-points-for-a-win rule in football," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 248-264, January.
    5. Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.
    6. Price, Ilan & Fowkes, Jaroslav & Hopman, Daniel, 2019. "Gaussian processes for unconstraining demand," European Journal of Operational Research, Elsevier, vol. 275(2), pages 621-634.
    7. Ingrid Nappi‐Choulet Pr. & Tristan‐Pierre Maury, 2009. "A Spatiotemporal Autoregressive Price Index for the Paris Office Property Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 37(2), pages 305-340, June.
    8. Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.

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