The econometric analysis of seasonal time series
The econometric analysis of seasonal time series
Editorial: 2nd Special Issue on Statistical Signal Extraction and Filtering
Computational Statistics & Data Analysis
Temporal aggregation, systematic sampling, and the Hodrick-Prescott filter
Computational Statistics & Data Analysis
Linear dynamic harmonic regression
Computational Statistics & Data Analysis
Forecasting binary longitudinal data by a functional PC-ARIMA model
Computational Statistics & Data Analysis
Removing seasonality under a changing regime: Filtering new car sales
Computational Statistics & Data Analysis
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The ARIMA-model-based methodology of programs TRAMO and SEATS is applied for seasonal adjustment and trend-cycle estimation of the exports, imports, and balance of trade Japanese series. The programs are used in an automatic mode, and the results are analyzed. It is shown how the SEATS output can be of help when discriminating among competing models. Finally, the example is used to discuss the important problem of the choice between direct and indirect adjustment of an aggregate. It is concluded that, because aggregation has a strong effect on the spectral shape of the series, and because seasonal adjustment is a non-linear transformation of the original series, direct adjustment is preferable, even at the cost of destroying identities between the original series.