Open AccessBayesian Methods for Completing Data in Spatial Models
Author(s) -
Wolfgang Polasek,
Carlos Llano,
Richard Sellner
Publication year - 2010
Publication title -
review of economic analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.101
H-Index - 1
ISSN - 1973-3909
DOI - 10.15353/rea.v2i2.1472
Subject(s) - contiguity , bayesian probability , econometrics , extrapolation , markov chain monte carlo , context (archaeology) , mathematics , covariance , statistics , spatial contextual awareness , computer science , geography , artificial intelligence , archaeology , operating system
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.