Bayesian Methods for Completing Data in Space-time Panel Models

Carlos LLano, Wolfgang Polasek () and Richard Philipp Sellner
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Carlos LLano: Departamento de Análisis Económico, Facultad de Ciencias Económicas y Empresariales, Universidad Autónoma de Madrid

No 241, Economics Series from Institute for Advanced Studies

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.

Keywords: Interpolation; Spatial panel econometrics; MCMC; Spatial Chow-Lin; Missing regional data; Spanish provinces; 'Polycentric-periphery' relationship (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 E17 R12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-geo and nep-ure
Date: 2009-07

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http://www.ihs.ac.at/publications/eco/es-241.pdf First version, 2009 (application/pdf)

Related works:
Working Paper: BAYESIAN METHODS FOR COMPLETING DATA IN SPACE-TIME PANEL MODELS (2009)
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Persistent link: http://EconPapers.repec.org/RePEc:ihs:ihsesp:241

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