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Difficulties in Seasonal Adjustment

Difficulties in Seasonal Adjustment. N. Alpay KOÇAK Turkish Statistical Institute. Difficulties in seasonal adjustment. In general manner, seasonal adjusment is a type of economic time series analysis .

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Difficulties in Seasonal Adjustment

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  1. Difficulties inSeasonal Adjustment N. Alpay KOÇAK Turkish Statistical Institute

  2. Difficulties in seasonal adjustment • In general manner, seasonal adjusment is a type of economic time series analysis . • Trend-Cycle, Seasonal, Transitory, Irregular, Calendar Effect, Outliers, Missing Observations, Forecasts etc. • Actually, one can use one of these components to interprete the properties of the economic time series.

  3. Difficulties in seasonal adjustment • Original series – seasonality = Seasonal Adjusted series (for short term economic analysis) • Trend-cycle component (for short-term economic analysis with smoother series) • Seasonal and calendar component (to understand underlying movements of the series in a year) • Outliers (to understand shocks and persistent effects on the series) • Etc...

  4. Classical Output of Demetra+

  5. Trend_Cycle = Trend + Cycle

  6. Seasonal Component

  7. Irregular component

  8. Trading Days (Fixed holidays included)) Trading days Parameter Value Std error T-Stat P-value Monday -0.0187 0.0105 -1.77 0.0861 Tuesday 0.0007 0.0097 0.07 0.9447 Wednesday -0.0103 0.0111 -0.93 0.3611 Thursday 0.0286 0.0097 2.94 0.0061 Friday -0.0126 0.0106 -1.19 0.2424 Saturday 0.0050 0.0103 0.49 0.6309 Sunday(derived)0.0072 0.0091 0.79 0.4334

  9. Difficulties in seasonal adjustment • Two main requests for performing good seasonal adjustment of a time series • Conceptual knowledge • Statistical and Econometric knowledge

  10. Difficulties in seasonal adjustment • Conceptual knowledge • Analytical Framework, Concepts, Definitions, and Classifications • Definition • Classification • Scope of the data • Methodologic issues • Geographical issues • Accounting Conventions • Unit • Valuation • Compilation Practices

  11. Statistical and Econometric knowledge • Basic data transformations • Model estimation (ARIMA model) • Significancy • Diagnostics • Spectrum graphics • Filters

  12. Transformation: Level & Log

  13. Statistical and Econometric knowledge • Basic data transformations • Model estimation (ARIMA model) • Significancy • Diagnostics • Spectrum graphics • Filters Not so much theoretical detail! Just to know Why and what they do?

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