Bayesian Methods for Completing Data in Spatial Models

Polasek, Wolfgang and Llano, Carlos and Sellner, Richard (2010) Bayesian Methods for Completing Data in Spatial Models. Review of Economic Analysis, 2 (2), pp. 194-214.

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Abstract or Table of Contents

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 covariance 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 between2000 and 2004. We assume that only NUTS-2 GDP is known and predict GDP at NUTS-3level by using socio-economic and spatial information available at NUTS-3. The spatial neighbourhood is defined by either km distance, travel time, contiguity or trade relationships. After running some sensitivity analysis, we present the forecast accuracy criteria comparing the predicted values with the observed ones.

Item Type: Article in Academic Journal
Classification Codes (e.g. JEL): C11, C15, C52, E17, R12
Status: Published
Date Deposited: 09 Jun 2015 11:11
Last Modified: 01 Apr 2016 14:19
URI: http://irihs.ihs.ac.at/id/eprint/3484

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