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Detection and Modeling of Trading Day Effects

Raymond J. Soukup (1) and David F. Findley (2)

ABSTRACT:

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.

KEYWORDS:

Spectral analysis, regARIMA models, model selection, forcasting, monthly time series





(1) Raymond J. Soukup is currently a statistican at the Naval Research Laboratories.

(2) David F. Findley was the Senior Mathematical Statistican for Time Series and is now a consultant, U. S. Census Bureau, 4600 Silver Hill Road, Washington, DC 20233. email : david.f.findley@census.gov



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Source: U.S. Census Bureau | Center for Statistical Research and Methodology | (301) 763-1649 (or x12@census.gov) |  Last Revised: November 19, 2012