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The Importance of Trend-Cycle Analysis for National Statistics Institutes/La importancia del análisis de ciclo-tendencia para los Institutos Nacionales de Estadística

Author

Listed:
  • MCLAREN, CRAIG H.

    () (University of Wollongong, Australia.)

  • ZHANG, XICHUAN (MARK)

    () (Australian Bureau of Statistics, Australia.)

Abstract

Seasonal adjustment is a widely applied statistical method. National Statistics Institutes around the world apply seasonal adjustment methods, such as X-12-ARIMA or TRAMO-SEATS, on a regular basis to help users interpret movements in the time series and aid in decision making. The seasonal adjustment process decomposes the original time series into three main components: a trend-cycle, seasonal and irregular. By definition the seasonally adjusted estimates still contain a degree of volatility as they are just a combination of the trend-cycle and irregular. Typically, as an analytical product, the seasonally adjusted estimates are published alongside the time series of the original estimates. In most countries the trend-cycle estimates are not published. Some countries, such as Australia, regularly publish trend-cycle as additional analytical product alongside the original and seasonally adjusted estimates to inform users. This paper presents the case for the regular calculation and production of trend-cycle estimates at National Statistics Institutes to help inform and educate users about the longer term signals in the time series. El ajuste estacional es un método estadístico muy extendido. En todo el mundo, los Institutos de Estadística recurren con frecuencia a métodos de ajuste estacional (como X-12-ARIMA o TRAMO-SEATS) para ayudar a los usuarios en la interpretación de los cambios en las series de tiempo y en la toma de decisiones. El ajuste estacional descompone la serie de tiempo original en tres componentes principales: ciclo-tendencia, estacional e irregular. Por su naturaleza, las estimaciones corregidas de estacionalidad mantienen un cierto grado de volatilidad ya que son una mezcla de ciclo-tendencia e irregularidades. A menudo, como un producto derivado, las estimaciones ajustadas estacionalmente se publican acompañando a las estimaciones originales de la serie de tiempo. En la mayoría de países, no se publican las estimaciones de ciclo-tendencia. No obstante, en algunos como Australia sí que se publican con regularidad como un producto adicional a las estimaciones originales y a las ajustadas estacionalmente y como información para los usuarios. Este artículo defiende la necesidad de calcular y publicar con regularidad las estimaciones de ciclo-tendencia por los Institutos Nacionales de Estadística con el objetivo de informar y educar a los usuarios sobre las señales a largo plazo en las series de tiempo.

Suggested Citation

  • Mclaren, Craig H. & Zhang, Xichuan (Mark), 2010. "The Importance of Trend-Cycle Analysis for National Statistics Institutes/La importancia del análisis de ciclo-tendencia para los Institutos Nacionales de Estadística," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 28, pages 607-624, Diciembre.
  • Handle: RePEc:lrk:eeaart:28_3_6
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    References listed on IDEAS

    as
    1. Findley, David F, et al, 1998. "New Capabilities and Methods of the X-12-ARIMA Seasonal-Adjustment Program," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(2), pages 127-152, April.
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    More about this item

    Keywords

    Ciclo-tendencia; Difusión estadística; Ajuste estacional; Institutos Nacionales de Estadística. ; Trend-cycle; Statistical dissemination; Seasonal adjustment; National Statistics Institutes.;

    JEL classification:

    • C30 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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