Bayesian Methods for Completing Data in Space-time Panel Models

Llano, Carlos and Polasek, Wolfgang and Sellner, Richard (June 2009) Bayesian Methods for Completing Data in Space-time Panel Models. IHS Economics Series 241

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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 andspatial 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.;

Item Type: IHS Series
Keywords: 'Interpolation' 'Spatial panel econometrics' 'MCMC' 'Spatial Chow-Lin' 'Missing regional data' 'Spanish provinces' ''Polycentric-periphery' relationship'
Classification Codes (e.g. JEL): C11, C15, C52, E17, R12
Status: Published
Date Deposited: 26 Sep 2014 10:38
Last Modified: 22 Jul 2017 17:12
URI: http://irihs.ihs.ac.at/id/eprint/1924

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