IDEAS home Printed from https://ideas.repec.org/p/ihs/ihsesp/295.html
   My bibliography  Save this paper

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://irihs.ihs.ac.at/id/eprint/2195
    File Function: First version, 2013
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Massimo Gerli & Giovanni Marini, 2006. "Spatial and Temporal Time Series Conversion: A Consistent Estimator of the Error Variance-Covariance Matrix," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2005(3), pages 373-405.
    2. Víctor Gómez & Félix Aparicio‐Pérez, 2009. "A new state–space methodology to disaggregate multivariate time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(1), pages 97-124, January.
    3. Gary Koop & Stuart McIntyre & James Mitchell & Aubrey Poon, 2018. "Regional Output Growth in the United Kingdom: More Timely and Higher Frequency Estimates, 1970-2017," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2018-14, Economic Statistics Centre of Excellence (ESCoE).
    4. Cuevas Ángel & Quilis Enrique M. & Espasa Antoni, 2015. "Quarterly Regional GDP Flash Estimates by Means of Benchmarking and Chain Linking," Journal of Official Statistics, Sciendo, vol. 31(4), pages 627-647, December.
    5. Ivonne Caridad Perez Correa & Juan Miguel Martinez Buendia, 2013. "Desagregación multivariada del PIB sectorial del departamento de Bolívar," Revista Economía y Región, Universidad Tecnológica de Bolívar, vol. 7(1), pages 139-167, June.
    6. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
    7. Carlos Llano & Wolfgang Polasek & Richard Sellner, 2009. "Bayesian Methods for Completing Data in Space-Time Panel Models," Working Paper series 05_09, Rimini Centre for Economic Analysis.
    8. Víctor Guerrero & Fabio Nieto, 1999. "Temporal and contemporaneous disaggregation of multiple economic time series," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 8(2), pages 459-489, December.
    9. Wolfgang Polasek & Richard Sellner, 2008. "Spatial Chow-Lin Methods: Bayesian And Ml Forecast Comparisons," Working Paper series 38_08, Rimini Centre for Economic Analysis.
    10. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    11. Mateusz Pipień & Sylwia Roszkowska, 2015. "Szacunki kwartalnego PKB w polskich województwach," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 5, pages 145-169.
    12. Kahouli, Sondès, 2011. "Re-examining uranium supply and demand: New insights," Energy Policy, Elsevier, vol. 39(1), pages 358-376, January.
    13. Valter Giacinto & Libero Monteforte & Andrea Filippone & Francesco Montaruli & Tiziano Ropele, 2021. "ITER: A Quarterly Indicator of Regional Economic Activity in Italy," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 7(1), pages 129-147, March.
    14. Baoline Chen & Tommaso Di Fonzo & Thomas Howells & Marco Marini, 2018. "The statistical reconciliation of time series of accounts between two benchmark revisions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 533-552, November.
    15. George Athanasopoulos & Rob J Hyndman & Nikolaos Kourentzes & Anastasios Panagiotelis, 2023. "Forecast Reconciliation: A Review," Monash Econometrics and Business Statistics Working Papers 8/23, Monash University, Department of Econometrics and Business Statistics.
    16. Gallego López, Nuria & Llano Verduras, Carlos & Perez García, Julian, 2010. "Estimación de los flujos de transporte de mercancías interregionales trimestrales mediante técnicas de interpolación temporal/Estimating Quarterly Interregional Commodity Transport Flows by Means of T," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 28, pages 699(38á.)-6, Diciembre.
    17. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    18. Suad Elezović & Yingfu Xie, 2018. "Reconciliation of seasonally adjusted data with applications to the Swedish quarterly national accounts," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 590-602, November.
    19. José Casals & Miguel Jerez & Sonia Sotoca, 2009. "Modelling and forecasting time series sampled at different frequencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(4), pages 316-342.
    20. Víctor M. Guerrero & Francisco Corona, 2018. "Retropolating some relevant series of Mexico's System of National Accounts at constant prices: The case of Mexico City's GDP," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 72(4), pages 495-519, November.

    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)

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ihs:ihsesp:295. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Doris Szoncsitz (email available below). General contact details of provider: https://edirc.repec.org/data/deihsat.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.