Abstract
This paper provides an overview of the R package gets, which contains facilities for automated general-to-specific (GETS) modeling of the mean and variance of a regression, and indicator saturation (IS) methods for the detection and modeling of outliers and structural breaks. The mean can be specified as an autoregressive model with covariates (an model), and the variance can be specified as an autoregressive log-variance model with covariates (a 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 modeling of the mean specification of an 'arx' object. The third function undertakes GETS modeling of the log-variance specification of an 'arx' object. The fourth function undertakes GETS modeling 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 LATEX code of the estimation output can readily be generated.
Highlights
General-to-specific (GETS) modeling combines well-known ingredients: backwards elimination, single and multiple hypothesis testing, goodness-of-fit measures and diagnostics tests. The way these are combined by GETS modeling enables rival theories and models to be tested gets: General-to-Specific Modeling and Indicator Saturation against each other, resulting in a parsimonious, statistically valid model that explains the characteristics of the data being investigated
This paper provides an overview of the R (R Core Team 2018) package gets (Sucarrat, Pretis, and Reade 2018), which contains facilities for automated general-to-specific (GETS) modeling of the mean and variance of cross-sectional and time series regressions, and indicator saturation (IS) methods for the detection and modeling of outliers and structural breaks in the mean
The results suggest a high impact of the autoregressive conditional heteroscedastic (ARCH)(1) and ARCH(2) terms – much higher than for financial returns,5 and that the conditional variance depends on quarter
Summary
General-to-specific (GETS) modeling combines well-known ingredients: backwards elimination, single and multiple hypothesis testing, goodness-of-fit measures and diagnostics tests. This paper provides an overview of the R (R Core Team 2018) package gets (Sucarrat, Pretis, and Reade 2018), which contains facilities for automated general-to-specific (GETS) modeling of the mean and variance of cross-sectional and time series regressions, and indicator saturation (IS) methods for the detection and modeling of outliers and structural breaks in the mean. The AR-X model with a log-ARCH-X error term, where the “X” refers to the covariates (the covariates need not be the same in the mean and variance specifications) It should be underlined, that gets is not limited to time series models (see Section 2.3): Static models (e.g., cross-sectional or panel) can be estimated by specifying the regression without dynamics.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
