Spatial Chow-Lin models for completing growth rates in cross-sections

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Reihe Ökonomie / Economics Series ; No. 295

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Model Evaluation, Validation, and Selection ; General Aggregative Models: Forecasting and Simulation: Models and Applications ; Size and Spatial Distributions of Regional Economic Activity
• Subject: interpolation ; missing disaggregated values in spatial econometrics ; MCMC ; spatial Chow-Lin methods ; predicting growth rates data ; spatial autoregression (SAR) ; forecast evaluation ; outliers ; Regionales Wachstum ; Räumliche Verteilung ; Prognoseverfahren ; Spanien

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Polasek, Wolfgang
• Event: Veröffentlichung
• (who): Institute for Advanced Studies (IHS)
• (where): Vienna
• (when): 2013

Further information

• Handle: http://hdl.handle.net/10419/72675
• Last update: 10.03.2025, 11:44 AM CET