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

  • Ángel Cuevas

    ()

  • Enrique M. Quilis

    ()

  • Antoni Espasa

    ()

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    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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    File URL: http://e-archivo.uc3m.es/bitstream/10016/12704/1/ws114130.pdf
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    Paper provided by Universidad Carlos III, Departamento de Estadística y Econometría in its series Statistics and Econometrics Working Papers with number ws114130.

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    Date of creation: Dec 2011
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    Handle: RePEc:cte:wsrepe:ws114130
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    1. Robert B. Litterman, 1983. "A random walk, Markov model for the distribution of time series," Staff Report 84, Federal Reserve Bank of Minneapolis.
    2. 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.
    3. Fernandez, Roque B, 1981. "A Methodological Note on the Estimation of Time Series," The Review of Economics and Statistics, MIT Press, vol. 63(3), pages 471-76, August.
    4. Lutkepohl, Helmut & Claessen, Holger, 1997. "Analysis of cointegrated VARMA processes," Journal of Econometrics, Elsevier, vol. 80(2), pages 223-239, October.
    5. 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.
    6. 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.
    7. Tommaso Proietti, 2011. "Multivariate temporal disaggregation with cross-sectional constraints," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1455-1466, June.
    8. Tommaso Proietti, 2004. "Temporal Disaggregation by State Space Methods: Dynamic Regression Methods Revisited," Econometrics 0411011, EconWPA.
    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. Maravall, Agustin, 1993. "Stochastic linear trends : Models and estimators," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 5-37, March.
    11. Antoni, Espasa & Iván, Mayo, 2011. "Forecasting aggregate and disaggregates with common features," Statistics and Econometrics Working Papers ws110805, Universidad Carlos III, Departamento de Estadística y Econometría.
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