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We illustrate the roles of three types of diagnostics for
detecting or confirming the presence of trading day effects and for
selecting among competing trading day models. These are spectral
analysis, AIC comparisons, and out-of-sample forecast-error
diagnostics. We show merits and limitations of each in the context
of a study of four alternative trading day models for fifty-eight
Census Bureau time series. We consider both direct estimation from
the observations and indirect estimation from the irregulars.
Spectral analysis, regARIMA models, model selection, forcasting,
monthly time series
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