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Bayesian Methods for Completing Data in Spatial Models

Author

Listed:
  • Wolfang Polasek

    (Institute for Advanced Studies, Stumpergasse 56, 1060 Vienna, Austria, and University of Porto, Rua Campo Alegre, Portugal)

  • Carlos Llano

    (Universidad Autonoma de Madrid, Facultad de Ciencias Economicas y Empresariales, Departamento de Analisis Economico, 28049 Madrid)

  • Richard Sellner

    (Institute for Advanced Studies, Stumpergasse 56, 1060 Vienna, Austria)

Abstract

Completing data sets that are collected in heterogeneous units is a quite frequent problem. Chow and Lin (1971) were the first to develop a unified framework for the three problems (interpolation, extrapolation and distribution) of predicting times series by related series (the 'indicators'). This paper develops a spatial Chow-Lin procedure for cross-sectional data and compares the classical and Bayesian estimation methods. We outline the error co- variance structure in a spatial context and derive the BLUE for ML and Bayesian MCMC estimation. In an example, we apply the procedure to Spanish regional GDP data between 2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3 level by using socio-economic and spatial information available at NUTS-3. The spatial neighborhood is defined by either km distance, travel time, contiguity or trade relation- ships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.

Suggested Citation

  • Wolfang Polasek & Carlos Llano & Richard Sellner, 2010. "Bayesian Methods for Completing Data in Spatial Models," Review of Economic Analysis, Digital Initiatives at the University of Waterloo Library, vol. 2(2), pages 194-214, June.
    • Handle: RePEc:ren:journl:v:2:y:2010:i:2:p:194-214
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    Citations

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    Cited by:

    1. Baltagi, Badi H. & Fingleton, Bernard & Pirotte, Alain, 2019. "A time-space dynamic panel data model with spatial moving average errors," Regional Science and Urban Economics, Elsevier, vol. 76(C), pages 13-31.
    2. Bernard Fingleton & Julie Gallo & Alain Pirotte, 2018. "A multidimensional spatial lag panel data model with spatial moving average nested random effects errors," Empirical Economics, Springer, vol. 55(1), pages 113-146, August.
    3. Vladimir Boyko & Nadezhda Kislyak & Mikhail Nikitin & Oleg Oborin, 2020. "Methods for Estimating the Gross Regional Product Leading Indicator," Russian Journal of Money and Finance, Bank of Russia, vol. 79(3), pages 3-29, September.
    4. Bernard Fingleton, 2020. "Exploring Brexit with dynamic spatial panel models: some possible outcomes for employment across the EU regions," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 64(2), pages 455-491, April.
    5. Barbara Batóg & Jacek Batóg, 2021. "Regional Government Revenue Forecasting: Risk Factors of Investment Financing," Risks, MDPI, vol. 9(12), pages 1-15, November.
    6. Mateusz Pipień & Sylwia Roszkowska, 2015. "Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters," NBP Working Papers 219, Narodowy Bank Polski.
    7. Bernard Fingleton, 2020. "Italexit, is it another Brexit?," Journal of Geographical Systems, Springer, vol. 22(1), pages 77-104, January.
    8. 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.
    9. Joanna Horabik & Zbigniew Nahorski, 2014. "Improving resolution of a spatial air pollution inventory with a statistical inference approach," Climatic Change, Springer, vol. 124(3), pages 575-589, June.
    10. 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

    Interpolation; Spatial Econometrics; MCMC; Spatial Chow-Lin; Missing Re- gional Data; Spatial Autoregression; Forecasting by MCMC; NUTS: Nomenclature of Ter- ritorial Units for Statistics;
    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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