Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/72675
Authors: 
Polasek, Wolfgang
Year of Publication: 
2013
Series/Report no.: 
Reihe Ökonomie / Economics Series, Institut für Höhere Studien (IHS) 295
Abstract: 
Growth rate data that are collected incompletely in cross-sections is a quite frequent problem. Chow and Lin (1971) have developed a method for predicting unobserved disaggregated time series and we propose an extension of the procedure for completing cross-sectional growth rates similar to the spatial Chow-Lin method of Liano et al. (2009). Disaggregated growth rates cannot be predicted directly and requires a system estimation of two Chow-Lin prediction models, where we compare classical and Bayesian estimation and prediction methods. We demonstrate the procedure for Spanish regional GDP growth rates between 2000 and 2004 at a NUTS-3 level. We evaluate the growth rate forecasts by accuracy criteria, because for the Spanish data-set we can compare the predicted with the observed values.
Subjects: 
interpolation
missing disaggregated values in spatial econometrics
MCMC
spatial Chow-Lin methods
predicting growth rates data
spatial autoregression (SAR)
forecast evaluation
outliers
JEL: 
C11
C15
C52
E17
R12
Document Type: 
Working Paper

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