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This document serves as a comprehensive guide to the X11 Seasonal Adjustment Method, detailing its application, syntax, examples, and historical context, aimed primarily at economic data analysis.
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In the Time Series platform, the X-11 Decomposition method adjusts the original time series using either a multiplicative or an additive decomposition. The model is fit using an iterative process to estimate the three X-11 components: trend cycle, seasonal, and irregular.
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This method, known as X-11-ARIMA, applies an ARIMA model to the original data (after adjustments, if any) to forecast the series one or more years. This extended series is then seasonally adjusted, allowing symmetric weights to be applied to the end of the original data.
SEATS filters are derived directly from the ARIMA model and hence are tailored to the specific properties of the series as reflected in the estimated model. In contrast, X11 selects from a set of predetermined filters that have been shown to work well for a wide variety of series.

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The X11 Procedure is a method used in statistical analysis for seasonal adjustment of time series data. It enables the extraction of seasonal components from data to improve forecasting and analysis.
Statistical agencies and organizations that collect and analyze time series data for economic, social, and financial assessments are required to file The X11 Procedure.
To fill out The X11 Procedure, one must gather relevant time series data, apply the X11 algorithm to decompose the data into seasonal, trend, and irregular components, and then report the adjusted figures as specified in the methodology.
The purpose of The X11 Procedure is to provide a systematic method for removing seasonal effects from time series data, allowing clearer analysis of underlying trends and economic cycles.
The X11 Procedure requires reporting of the original time series data, the seasonal factors calculated, the adjusted data, and details on the methodology and assumptions used in the analysis.
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