Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72728
Authors: 
Polasek, Wolfgang
Sellner, Richard
Year of Publication: 
2010
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 255
Abstract: 
Flow data across regions can be modeled by spatial econometric models, see LeSage and Pace (2009). Recently, regional studies became interested in the aggregation and disaggregation of flow models, because trade data cannot be obtained at a disaggregated level but data are published on an aggregate level. Furthermore, missing data in disaggregated flow models occur quite often since detailed measurements are often not possible at all observation points in time and space. In this paper we develop classical and Bayesian methods to complete flow data. The Chow and Lin (1971) method was developed for completing disaggregated incomplete time series data. We will extend this method in a general framework to spatially correlated flow data using the cross-sectional Chow-Lin method of Polasek et al. (2009). The missing disaggregated data can be obtained either by feasible GLS prediction or by a Bayesian (posterior) predictive density.
Subjects: 
missing values in spatial econometrics, MCMC
non-spatial Chow-Lin (CL) and spatial Chow-Lin (SCL) methods
spatial internal flow (SIF) models
origin and destination (OD) data
JEL: 
C11
C15
C52
E17
R12
Document Type: 
Working Paper

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