Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72683
Authors: 
Llano, Carlos
Polasek, Wolfgang
Sellner, Richard
Year of Publication: 
2009
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 241
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.
Subjects: 
interpolation
spatial panel econometrics
MCMC
spatial Chow-Lin
missing regional data
Spanish provinces
'polycentric-periphery' relationship
JEL: 
C11
C15
C52
E17
R12
Document Type: 
Working Paper

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