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Data imputation in a short-run space-time series: A Bayesian approach

Author

Listed:
  • Lars Pforte
  • Chris Brunsdon
  • Conor Cahalane
  • Martin Charlton

Abstract

This paper discusses a project on the completion of a database of socio-economic indicators across the European Union for the years from 1990 onward at various spatial scales. Thus the database consists of various time series with a spatial component. As a substantial amount of the data was missing a method of imputation was required to complete the database. A Markov Chain Monte Carlo approach was opted for. We describe the Markov Chain Monte Carlo method in detail. Furthermore, we explain how we achieved spatial coherence between different time series and their observed and estimated data points.

Suggested Citation

  • Lars Pforte & Chris Brunsdon & Conor Cahalane & Martin Charlton, 2018. "Data imputation in a short-run space-time series: A Bayesian approach," Environment and Planning B, , vol. 45(5), pages 864-887, September.
    • Handle: RePEc:sae:envirb:v:45:y:2018:i:5:p:864-887
    DOI: 10.1177/0265813516688688
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    References listed on IDEAS

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    1. Richard J. Cook & Leilei Zeng & Grace Y. Yi, 2004. "Marginal Analysis of Incomplete Longitudinal Binary Data: A Cautionary Note on LOCF Imputation," Biometrics, The International Biometric Society, vol. 60(3), pages 820-828, September.
    2. Terry Gleason & Richard Staelin, 1975. "A proposal for handling missing data," Psychometrika, Springer;The Psychometric Society, vol. 40(2), pages 229-252, June.
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