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Seasonal Adjustment Based on ARIMA Model Decomposition: TRAMO-SEATS

  • Estela Bee Dagum
  • Silvia Bianconcini
Chapter
Part of the Statistics for Social and Behavioral Sciences book series (SSBS)

Abstract

TRAMO-SEATS is a seasonal adjustment method based on ARIMA modeling. TRAMO estimates via regression dummy variables and direct ARIMA modeling (regARIMA) the deterministic components, trading days, moving holidays, and outliers, which are later removed from the input data. In a second round, SEATS estimates the stochastic components, seasonality, and trend-cycle, from an ARIMA model fitted to the data where the deterministic components are removed. SEATS uses the filters derived from the ARIMA model that describes the behavior of the time series. By imposing certain conditions, a unique canonical decomposition is performed to obtain the ARIMA models for each component. This chapter discusses with details the estimation methods used by TRAMO and SEATS as well as the basic assumptions on the derivation of ARIMA models for each component. An illustrative example of the seasonal adjustment with the TRAMO-SEATS software default option is shown with the US New Orders for Durable Goods series. The illustrative example concentrates on the most important tables of this software that enable to assess the quality of the seasonal adjustment.

Keywords

Outlier Detection Minimum Mean Square Error ARIMA Model Seasonal Component Seasonal Adjustment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Estela Bee Dagum
    • 1
  • Silvia Bianconcini
    • 1
  1. 1.Department of Statistical SciencesUniversity of BolognaBolognaItaly

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