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Activity Number: 101 - Time Series Modeling: Mixed Frequency Data, Seasonality, and Model Identification
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #322230
Title: Penalized M-Estimation of Autocorrelation for Time Series Goodness of Fit
Author(s): Colin M Gallagher* and Xiyan Tan
Companies: Clemson University and Clemson University
Keywords: autocorrelation; ARCH; goodness of fit; ARMA; shrinkage; partial correlation

It has long been known that the sample autocorrelation underestimates the magnitude of correlation in stationary time series. Although finite sample bias corrections can be found under specific assumed models, no general formulae are available. We introduce a novel penalized M-estimator for (partial) autocorrelation, with the penalty pushing the estimator toward a target selected from the data. This both encapsulates and differs from previous attempts at penalized estimation for autocorrelation, which shrink the estimator toward the target value of zero. Target and tuning parameters can be selected to improve time series Portmanteau tests--shrinking small magnitude correlations toward zero controls type I error, while increasing larger magnitude correlations improves power. Specific data based choices for target and tuning parameters are provided for general classes of time series goodness of fit tests. Simulations show power is improved for the most prevalent tests from the literature and the proposed methods are applied to data

Authors who are presenting talks have a * after their name.

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