Research Reports

The Multiple Testing Problem for Box-Pierce Statistics

Tucker McElroy and Brian Monsell

KEY WORDS:

ARIMA models, Ljung-Box statistic, Time series residuals.

ABSTRACT

We derive the exact joint asymptotic distribution for multiple Box-Pierce statistics, and use these results to determine appropriate critical values in joint testing problems of time series goodness-of-fit. A novel alpha-rationing scheme, motivated by the sequence of conditional probabilities for the statistical tests, is developed and implemented. This method can be used to produce critical values and p-values both for each step of the sequential testing procedure, and for the procedure as a whole. Efficient computational algorithms are discussed. Simulation studies assess the impact of finite samples on the real Type I error. It is also demonstrated empirically that the conventional x-squared critical values for the Box-Pierce statistics are too small, with a Type I error rate greater than the nominal; the new method does not suffer from this defect, and allows for more rigorous model-building.

CITATION:

Tucker McElroy and Brian Monsell. (2012). The Multiple Testing Problem for Box-Pierce Statistics. Center for Statistical Research & Methodology, Research and Methodology Directorate Research Report Series (Statistics #2012-15). U.S. Census Bureau. Available online at <http://www.census.gov/srd/papers/pdf/rrs2012-15.pdf>.

Source: U.S. Census Bureau, Center for Statistical Research & Methodology, Research and Methodology Directorate

Published online: September 24, 2012
Last revised: September 24, 2012