Estimation of Regional Transition Probabilities for Spatial Dynamic Microsimulations from Survey Data Lacking in Regional Detail

Jan Pablo Burgard, Joscha Krause and Simon Schmaus

No 2019-12, Research Papers in Economics from University of Trier, Department of Economics

Abstract: Spatial dynamic microsimulations allow for the multivariate analysis of complex socio- economic systems with geographic segmentation. For this, a synthetic replica of the system as base population is stochastically projected into future periods. Thereby, the projection is based on micro-level transition probabilities. They need to accurately represent the characteristic dynamics of the system to allow for reliable simulation outcomes. In practice, transition probabilities are unknown and must be estimated from suitable survey data. This can be challenging when the characteristic dynamics vary locally. Survey data often lacks in regional detail due to confidentiality restrictions and limited sampling resources. In that case, transition probability estimates may misrepresent local dynamics as a result of insufficient local observations and coverage problems. The simulation process then fails to provide an authentic evolution. We present two transition probability estimation techniques that account for regional heterogeneity when the survey data lacks in regional detail. Using methods of constrained optimization and ex-post alignment, we show that local micro level transition dynamics can be accurately recovered from aggregated regional benchmarks. The techniques are compared in theory and subsequently tested in a simulation study.

Keywords: Constrained Maximum Likelihood; Logit Scaling; Spatiotemporal Modelling; Regional Benchmark (search for similar items in EconPapers)
Pages: 39 pages
Date: 2019
New Economics Papers: this item is included in nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:trr:wpaper:201912

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