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The X-II-ARIMA seasonal adjustment method
The X-II-ARIMA seasonal adjustment method
Introduction
The majority of seasonal adjustment methods so far developed are based on univariate time series models. They are selected mainly for their simplicity and can be applied without specialized knowledge in a subject matter field.
A few attempts have been made to estimate seasonals based on causal explanations but none of them reached further than the experimental stage. Mendershausen (1939), for instance, tried to regress the seasonal for each month on a set of exogenous variables (meteorological and social variates) in order to build an explanatory model for seasonality but his empirical results were inconclusive.
Others in Series
Working Paper
Computation of the Autocovariances for Time Series
Gegenbauer processes allow for flexible and convenient modeling of time series data with multiple spectral peaks.
Working Paper
The X-11 Variant of the Census Method II Seasonal Adjustment Program
There are various and sundry methods for seasonally adjusting economic time series.
Working Paper
Computation of Vector ARMA Autocovariances
Describes an algorithm for computing the autocovariance sequence of a VARMA
process without requiring the intermediary determination of the Wold representation.
Related Information
Page Last Revised - October 8, 2021
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