Spatial Chow-Lin Models for Completing Growth Rates in Cross-sections

Wolfgang Polasek

No 295, Economics Series from Institute for Advanced Studies

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.

Keywords: Interpolation; missing disaggregated values in spatial econometrics; MCMC; Spatial Chow-Lin methods; predicting growth rates data; spatial autoregression (SAR); forecast evaluation; outliers (search for similar items in EconPapers)
JEL-codes: C11 C15 C52 E17 R12 (search for similar items in EconPapers)
Pages: 29 pages
Date: 2013-04
New Economics Papers: this item is included in nep-ecm, nep-for, nep-geo and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://irihs.ihs.ac.at/id/eprint/2195 First version, 2013 (application/pdf)

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