Seasonal Adjustment Methods : An Application to the Turkish Monetary Aggregates
Seasonality can be defined as a pattern of a time series, which repeats at regular intervals every year. Seasonal fluctuations in data make it difficult to analyse whether changes in data for a given period reflect important increases or decreases in the level of the data, or are due to regularly occurring variation. In search for the economic measures that are independent of seasonal variations, methods had been developed to remove the effect of seasonal changes from the original data to produce seasonally adjusted data. The seasonally adjusted data, providing more readily interpretable measures of changes occurring in a given period, reflects real economic movements without the misleading seasonal changes. The choice of method for seasonal adjustment is crucial for the removal of all seasonal effects in the data. Seasonal adjustment is normally done using the off-the-shelf programs-most commonly worldwide by one of the programs in the X-11 family, X-12 ARIMA, the latest improved version. Another program in common use is the TRAMO/SEATS package developed by the Bank of Spain and promoted by Eurostat. In this study, the performances of two seasonal adjustment methods, X-12 ARIMA and TRAMO/SEATS, on the monetary aggregates will be studied. In section five, the two methods are applied to the M2 monetary aggregate series, and the resulting seasonally adjusted series are compared using specific criteria. In sections six and seven, some of the issues that should be concerned in the process of seasonal adjustment, are discussed.
Volume (Year): 2 (2002)
Issue (Month): 1 ()
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- Alberto Cabrero, 2000. "Seasonal Adjustment in Economic Time Series: the Experience of the Banco de España (with the model-based method)," Working Papers 0002, Banco de España;Working Papers Homepage.
- Maravall, Agustín & Kaiser, Regina, 2000. "Notes on time serie analysis, ARIMA models and signal extraction," DES - Working Papers. Statistics and Econometrics. WS 10058, Universidad Carlos III de Madrid. Departamento de Estadística.
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- Regina Kaiser & Agustín Maravall, 2000. "Notes on Time Series Analysis, ARIMA Models and Signal Extraction," Working Papers 0012, Banco de España;Working Papers Homepage.
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