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

Author

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  • Wolfgang Polasek

    (Department of Economics and Finance, Institute for Advanced Studies, Vienna, Austria and University of Porto, Portugal)

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.

Suggested Citation

  • Wolfgang Polasek, 2013. "Spatial Chow-Lin Models for Completing Growth Rates in Cross-sections," Economics Series 295, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:295
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    File URL: https://irihs.ihs.ac.at/id/eprint/2195
    File Function: First version, 2013
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    References listed on IDEAS

    as
    1. Di Fonzo, Tommaso, 1990. "The Estimation of M Disaggregate Time Series When Contemporaneous and Temporal Aggregates Are Known," The Review of Economics and Statistics, MIT Press, vol. 72(1), pages 178-182, February.
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    Cited by:

    1. Mateusz Pipień & Sylwia Roszkowska, 2015. "Quarterly estimates of regional GDP in Poland – application of statistical inference of functions of parameters," NBP Working Papers 219, Narodowy Bank Polski.

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    More about this item

    Keywords

    Interpolation; missing disaggregated values in spatial econometrics; MCMC; Spatial Chow-Lin methods; predicting growth rates data; spatial autoregression (SAR); forecast evaluation; outliers;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)

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