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Diagnostics for ARIMA-Model-Based Seasonal Adjustment
Diagnostics for ARIMA-Model-Based Seasonal Adjustment
Abstract
We present two types of diagnostics for SEATS and similar programs. We start with modifications of the diagnostic used by SEATS to detect underestimation or overestimation, meaning inadequate or excessive supression of frequency components near the target frequencies, e.g. the seasonal frequencies in the case of seasonal adjustment. The modifications use time-varying variances of the finite-length filter output instead of the constant variance associated with the infinite-length filter. The SEATS diagnostic is shown to be substantially biased toward indicating underestimation even when the estimation is optimal, a situation where the modified diagnostics are unbiased. The second type of diagnostic considered is an adaptation of the widely used sliding spans diagnostic of X-12-ARIMA. The adaptation is a method for determining the span length appropriate for model-based-adjustment as a function of the ARIMA model's seasonal moving average parameter.
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A Fortran Program for Regression Models with ARIMA Component Errors
RegComponent models are time series models with linear regression mean functions and error terms that follow ARIMA component time series models.
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Diagnostics for Model-Based Seasonal Adjustment
In this paper, we try to identify promising diagnostics for model-based seasonal adjustment.
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Page Last Revised - October 8, 2021
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