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

Author

Listed:
  • Polasek, Wolfgang

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria, and Faculty of Science, University of Porto, Porto, Portugal)

  • Sellner, Richard

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria)

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.

Suggested Citation

  • Polasek, Wolfgang & Sellner, Richard, 2010. "Spatial Chow-Lin Methods for Data Completion in Econometric Flow Models," Economics Series 255, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:255
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    File URL: https://irihs.ihs.ac.at/id/eprint/2016
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    Citations

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    Cited by:

    1. Adolfo Maza & José Villaverde, 2015. "A New FDI Potential Index: Design and Application to the EU Regions," European Planning Studies, Taylor & Francis Journals, vol. 23(12), pages 2535-2565, December.
    2. Wolfgang Polasek & Richard Sellner, 2013. "The Does Globalization Affect Regional Growth? Evidence for NUTS-2 Regions in EU-27," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 1, pages 23-65, March.
    3. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, Sciendo, vol. 31(4), pages 627-647, December.
    4. F. Bavaud & M. Kordi & C. Kaiser, 2018. "Flow autocorrelation: a dyadic approach," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 61(1), pages 95-111, July.
    5. Villaverde, José & Maza, Adolfo, 2015. "The determinants of inward foreign direct investment: Evidence from the European regions," International Business Review, Elsevier, vol. 24(2), pages 209-223.

    More about this item

    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;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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