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Desagregación temporal de series económicas con programación lineal

Author

Listed:
  • Luis Frank

    (Instituto Nacional de Estadística y Censos (INDEC); Universidad de Buenos Aires)

Abstract

El artículo presenta un método de desagregación temporal de series de tiempo, que combina la interpolación de datos de baja frecuencia con una o más series relacionadas de alta frecuencia. La serie desagregada es, esencialmente, la solución a un programa lineal que minimiza la suma de desvíos absolutos con la serie de baja frecuencia y con series relacionadas de alta frecuencia. El método es útil para conciliar series de baja frecuencia con otras relacionadas de alta frecuencia, cuando estas últimas presentan valores atípicos, datos faltantes o, incluso, cuando presentan distintas frecuencias. El nuevo método se pone a prueba desagregando la serie trimestral del VAB industrial con esta componente del Estimador Mensual de Actividad Económica.

Suggested Citation

  • Luis Frank, 2019. "Desagregación temporal de series económicas con programación lineal," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(1), pages 59-82, Octubre.
  • Handle: RePEc:atw:epecon:v:3:y:2019:i:1:p:59-82
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    File URL: https://erevistas.uca.edu.ar/index.php/ENSAYOS/article/view/2283/2115
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    References listed on IDEAS

    as
    1. Chow, Gregory C & Lin, An-loh, 1971. "Best Linear Unbiased Interpolation, Distribution, and Extrapolation of Time Series by Related Series," The Review of Economics and Statistics, MIT Press, vol. 53(4), pages 372-375, November.
    2. Marcos Dal Bianco & Jaime Martinez-Martín & Maximo Camacho, 2013. "Short-Run Forecasting of Argentine GDP Growth," Working Papers 1314, BBVA Bank, Economic Research Department.
    3. James Mitchell & Richard J. Smith & Martin R. Weale & Stephen Wright & Eduardo L. Salazar, 2005. "An Indicator of Monthly GDP and an Early Estimate of Quarterly GDP Growth," Economic Journal, Royal Economic Society, vol. 115(501), pages 108-129, February.
    4. J. C. G. Boot & W. Feibes & J. H. C. Lisman, 1967. "Further Methods of Derivation of Quarterly Figures from Annual Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 16(1), pages 65-75, March.
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    Keywords

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    JEL classification:

    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access

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