Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/72728 
Erscheinungsjahr: 
2010
Schriftenreihe/Nr.: 
Reihe Ökonomie / Economics Series No. 255
Verlag: 
Institute for Advanced Studies (IHS), Vienna
Zusammenfassung: 
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.
Schlagwörter: 
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
Dokumentart: 
Working Paper

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