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Combining benchmarking and chain-linking for short-term regional forecasting


  • Espasa, Antoni
  • Cuevas, Ángel
  • Quilis, Enrique M.


In this paper we propose a methodology to estimate and forecast the GDP of the different regions of a country, providing quarterly profiles paper offers a new instrument for short degree of synchronicity among regional business cycles. Technically, we combine time series models with benchma quarterly indicators and to estimate quarterly regional GDPs ensuring their temporal and transversal consistency with the National Accounts data. The methodology addresses the issue of non-additivity taking into account linked volume indexes used by the National Accounts and provides an efficient combination of structural as well as short-term information. The methodology is illustrated by an application to the quarterly GDP estimates and forecasts at the regional level (i.e., with a minimum compilation delay with respect to the national quarterly GDP)

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  • Espasa, Antoni & Cuevas, Ángel & Quilis, Enrique M., 2011. "Combining benchmarking and chain-linking for short-term regional forecasting," DES - Working Papers. Statistics and Econometrics. WS ws114130, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws114130

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    References listed on IDEAS

    1. Santos Silva, J. M. C. & Cardoso, F. N., 2001. "The Chow-Lin method using dynamic models," Economic Modelling, Elsevier, vol. 18(2), pages 269-280, April.
    2. Lutkepohl, Helmut & Claessen, Holger, 1997. "Analysis of cointegrated VARMA processes," Journal of Econometrics, Elsevier, vol. 80(2), pages 223-239, October.
    3. Espasa, Antoni & Mayo-Burgos, Iván, 2013. "Forecasting aggregates and disaggregates with common features," International Journal of Forecasting, Elsevier, vol. 29(4), pages 718-732.
    4. Bewley, Ronald & Orden, David & Yang, Minxian & Fisher, Lance A., 1994. "Comparison of Box--Tiao and Johansen canonical estimators of cointegrating vectors in VEC(1) models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 3-27.
    5. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
    6. Tommaso Proietti, 2006. "Temporal disaggregation by state space methods: Dynamic regression methods revisited," Econometrics Journal, Royal Economic Society, vol. 9(3), pages 357-372, November.
    7. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    8. Litterman, Robert B, 1983. "A Random Walk, Markov Model for the Distribution of Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(2), pages 169-173, April.
    9. Bewley, R. & Orden, D. & Fisher, L., 1991. "Box TIAO and Johansen Canonical Estimators of Cointegrating Vectors," Papers 91-5, New South Wales - School of Economics.
    10. Proietti, Tommaso, 2008. "Estimation of Common Factors under Cross-Sectional and Temporal Aggregation Constraints: Nowcasting Monthly GDP and its Main Components," MPRA Paper 6860, University Library of Munich, Germany.
    11. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
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    Cited by:

    1. Marcus Cobb, 2014. "GDP Forecasting Bias due to Aggregation Inaccuracy in a Chain- Linking Framework," Working Papers Central Bank of Chile 721, Central Bank of Chile.

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    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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