Abstract
General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for conditional forecasting and counterfactual analysis, whereas Indicator Saturation (ISAT) is a powerful and flexible approach to the detection and estimation of structural breaks (e.g. changes in parameters), and to the detection of outliers. To these ends, multi-path backwards elimination, single and multiple hypothesis tests on the coefficients, diagnostics tests and goodness-of-fit measures are combined to produce a parsimonious final model. In many situations a specific model or estimator is needed, a specific set of diagnostics tests may be required, or a specific fit criterion is preferred. In these situations, if the combination of estimator/model, diagnostics tests and fit criterion is not offered by publicly available software, then the implementation of user-specified GETS and ISAT methods puts a large programming-burden on the user. Generic functions and procedures that facilitate the implementation of user-specified GETS and ISAT methods for specific problems can therefore be of great benefit. The R package gets, version 0.20 (September 2019), is the first software - both inside and outside the R universe - to provide a complete set of facilities for user-specified GETS and ISAT methods: User-specified model/estimator, user-specified diagnostics and user-specified goodness-of-fit criteria. The aim of this article is to illustrate how user-specified GETS and ISAT methods can be implemented. The aim of this article is to illustrate how user-specified GETS and ISAT methods can be implemented with the R package gets.
Highlights
General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for scenario analysis, e.g. conditional forecasting and counterfactual analysis
If the combination of estimator/model, diagnostics tests and fit criterion is not offered by the publicly available softwares, the implementation of user-specified GETS and Indicator Saturation (ISAT) methods puts a large programming-burden on the user
Generic functions and procedures that facilitate the implementation of user-specified GETS and ISAT methods for specific problems can be of great benefit
Summary
General-to-Specific (GETS) modelling provides a comprehensive, systematic and cumulative approach to modelling that is ideally suited for scenario analysis, e.g. conditional forecasting and counterfactual analysis. The model selection properties of GETS and ISAT methods are summarised This is followed by a section that outlines the general principles of how user-specified estimation, user-specified diagnostics and user-specified goodnessof-fit measures are implemented. If the result (i.e. a list) returned from the user-specified estimator does not have the same structure as that returned from the default estimator ols (part of the gets package), arx, getsm and isat may not always work as expected. This is the case with respect to their extraction functions (e.g. print, coef, residuals and predict). While the name of the function is arbitrary, the first argument should be the regressand and the second the matrix of covariates
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