Automated General-to-Specific (GETS) regression modeling and indicator saturation methods for the detection of outliers and structural breaks

Pretis, F., Reade, J. ORCID: https://orcid.org/0000-0002-8610-530X and Sucarrat, G. (2018) Automated General-to-Specific (GETS) regression modeling and indicator saturation methods for the detection of outliers and structural breaks. Journal of Statistical Software, 86 (3). ISSN 1548-7660 doi: 10.18637/jss.v086.i03

Abstract/Summary

This paper provides an overview of the R package gets, which contains facilities for automated General-to-Specific (GETS) modelling of the mean and variance of a regression, and Indicator Saturation (IS) methods for the detection and modelling of outliers and structural breaks. The mean can be specified as an autoregressive model with covariates (an ‘AR-X’ model), and the variance can be specified as an autoregressive log-variance model with covariates (a ‘log-ARCH-X’ model). The covariates in the two specifications need not be the same, and the classical linear regression model is obtained as a special case when there is no dynamics, and when there are no covariates in the variance equation. The four main functions of the package are arx, getsm, getsv and isat. The first function estimates an AR-X model with log-ARCH-X errors. The second function undertakes GETS modelling of the mean specification of an arx object. The third function undertakes GETS modelling of the log-variance specification of an arx object. The fourth function undertakes GETS modelling of an indicator-saturated mean specification allowing for the detection of outliers and structural breaks. The usage of two convenience functions for export of results to EViews and STATA are illustrated, and LATEXcode of the estimation output can readily be generated.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/73481
Item Type	Article
Refereed	Yes
Divisions	Arts, Humanities and Social Science > School of Politics, Economics and International Relations > Economics
Publisher	Foundation for Open Access Statistics
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