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Spatial Chow-Lin Methods for Data Completion in Econometric Flow Models

Wolfgang Polasek and Richard Sellner

No 255, Economics Series from Institute for Advanced Studies

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.

Keywords: 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 (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 E17 R12 (search for similar items in EconPapers)
Pages: 28 pages
Date: 2010-09
New Economics Papers: this item is included in nep-ecm, nep-geo and nep-ure
References: Add references at CitEc
Citations: View citations in EconPapers (6)

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