IDEAS home Printed from https://ideas.repec.org/p/cte/derepe/3004.html

Un nuevo indicador semanal y mensual de actividad basado en el consumo de energía eléctrica

Author

Listed:
  • Cancelo, José Ramón
  • Espasa, Antoni

Abstract

La falta de información mensual o trimestral sobre el PIB obliga a utilizar una serie de indicadores parciales para el seguimiento a corto plazo de la actividad. De entre ellos destaca el consumo de energía eléctrica; sin embargo, la evolución de esta magnitud aparece muy distorsionada por las condiciones metereológicas y de calendario. En este trabajo se propone utilizar la información contenida en un modelo de predicción diaria del consumo eléctrico para estimar una serie diaria depurada de la demanda diferencial debida a estos factores; por agregación de dicha serie diaria corregida se obtienen indicadores más fiables de actividad semanal y mensual.

Suggested Citation

  • Cancelo, José Ramón & Espasa, Antoni, 1991. "Un nuevo indicador semanal y mensual de actividad basado en el consumo de energía eléctrica," DE - Documentos de Trabajo. Economía. DE 3004, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:derepe:3004
    as

    Download full text from publisher

    File URL: https://e-archivo.uc3m.es/rest/api/core/bitstreams/6b0f83a3-de1b-47b8-b110-3fb3d5200a62/content
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zellner, Arnold & Palm, Franz, 1974. "Time series analysis and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 2(1), pages 17-54, May.
    Full references (including those not matched with items on IDEAS)

    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. Anna Mikusheva & Mikkel Sølvsten, 2025. "Linear regression with weak exogeneity," Quantitative Economics, Econometric Society, vol. 16(2), pages 367-403, May.
    2. Umberto Triacca, 2016. "Measuring the Distance between Sets of ARMA Models," Econometrics, MDPI, vol. 4(3), pages 1-11, July.
    3. Cubadda, Gianluca & Hecq, Alain & Palm, Franz C., 2009. "Studying co-movements in large multivariate data prior to multivariate modelling," Journal of Econometrics, Elsevier, vol. 148(1), pages 25-35, January.
    4. Gupta, Rangan & Kabundi, Alain & Miller, Stephen M., 2011. "Forecasting the US real house price index: Structural and non-structural models with and without fundamentals," Economic Modelling, Elsevier, vol. 28(4), pages 2013-2021, July.
    5. Pami Dua & Nishita Raje & Satyananda Sahoo, 2004. "Interest Rate Modeling and Forecasting in India," Occasional papers 3, Centre for Development Economics, Delhi School of Economics.
    6. Kapetanios, G. & Pagan, A. & Scott, A., 2007. "Making a match: Combining theory and evidence in policy-oriented macroeconomic modeling," Journal of Econometrics, Elsevier, vol. 136(2), pages 565-594, February.
    7. Franses, Philip Hans, 2008. "Merging models and experts," International Journal of Forecasting, Elsevier, vol. 24(1), pages 31-33.
    8. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    9. J. S. Mehta & G. V. L. Narasimham & P. A. V. B. Swamy, 1975. "Estimation of a dynamic demand function for gasoline with different schemes of parameter variation," International Finance Discussion Papers 70, Board of Governors of the Federal Reserve System (U.S.).
    10. Francisco F. R. Ramos, 1996. "Forecasting market shares using VAR and BVAR models: A comparison of their forecasting performance," Econometrics 9601003, University Library of Munich, Germany.
    11. Hsiao, Cheng & Fujiki, Hiroshi, 1998. "Nonstationary Time-Series Modeling versus Structural Equation Modeling: With an Application to Japanese Money Demand," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 16(1), pages 57-79, May.
    12. René Capitelli, 1985. "Eine empirische Untersuchung über den Zusammenhang von kurz-, mittel- und langfristigen schweizerischen Zinssätzen," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 121(I), pages 1-22, March.
    13. Pauwels, Koen H., 2018. "Modeling Dynamic Relations Among Marketing and Performance Metrics," Foundations and Trends(R) in Marketing, now publishers, vol. 11(4), pages 215-301, November.
    14. Jonas Krampe & Luca Margaritella, 2021. "Factor Models with Sparse VAR Idiosyncratic Components," Papers 2112.07149, arXiv.org, revised May 2022.
    15. Boswijk, H. Peter & Franses, Philip Hans & van Dijk, Dick, 2010. "Cointegration in a historical perspective," Journal of Econometrics, Elsevier, vol. 158(1), pages 156-159, September.
    16. Adrian Pagan, 2001. "The Getting of Macroeconomic Wisdom," International Economic Association Series, in: Jacques Drèze (ed.), Advances in Macroeconomic Theory, chapter 11, pages 219-235, Palgrave Macmillan.
    17. Bauer, Dietmar & Wagner, Martin, 2009. "Using subspace algorithm cointegration analysis: Simulation performance and application to the term structure," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 1954-1973, April.
    18. Nijman, Theo & Sentana, Enrique, 1996. "Marginalization and contemporaneous aggregation in multivariate GARCH processes," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 71-87.
    19. Paul Lau, Sau-Him, 1999. "I(0) In, integration and cointegration out:: Time series properties of endogenous growth models," Journal of Econometrics, Elsevier, vol. 93(1), pages 1-24, November.

    More about this item

    Keywords

    ;

    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:cte:derepe:3004. 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: Ana Poveda (email available below). General contact details of provider: http://www.eco.uc3m.es/ .

    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.