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

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  • 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
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    References listed on IDEAS

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    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.
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    Tasas de crecimiento;

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