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

Lars Pforte, Chris Brunsdon, Conor Cahalane and Martin Charlton

Environment and Planning B, 2018, vol. 45, issue 5, 864-887

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.

Keywords: Time series; Markov Chain Monte Carlo; data imputation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:45:y:2018:i:5:p:864-887

DOI: 10.1177/0265813516688688

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