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Bayesian Methods for Completing Data in Space-time Panel Models

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Author Info

  • Llano, Carlos

    (Departamento de Análisis Económico, Facultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid)

  • Polasek, Wolfgang

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

  • Sellner, Richard

    (Department of Economics and Finance, Institute for Advanced Studies, 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 united 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 and panel data and compares the classical and Bayesian estimation methods. We outline the error covariance structure in a spatial context and derive the BLUE for the ML and Bayesian MCMC estimation. Finally, we apply the procedure to Spanish regional GDP data between 2000-2004. We assume that only NUTS-2 GDP is known and predict GDPat 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 and trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted with the observed values.

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File URL: http://www.ihs.ac.at/publications/eco/es-241.pdf
File Function: First version, 2009
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Bibliographic Info

Paper provided by Institute for Advanced Studies in its series Economics Series with number 241.

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Length: 38 pages
Date of creation: Jul 2009
Date of revision:
Handle: RePEc:ihs:ihsesp:241

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Related research

Keywords: Interpolation; Spatial panel econometrics; MCMC; Spatial Chow-Lin; Missing regional data; Spanish provinces; 'Polycentric-periphery' relationship;

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