User-Specified General-to-Specific and Indicator Saturation Methods

Authors

Genaro Sucarrat, BI Norwegian Business School

Date of this Version

12-2020

Document Type

Article

Citation

The R Journal (December 2020) 12(2); Editor: Michael J. Kane

Comments

Copyright 2020, The R Foundation. Open access material. License: CC BY 4.0 International

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 andgoodness-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 f it criterion is preferred. In these situations, if the combination of estimator/model, diagnostics tests and fit criterion is not offered in a pre-programmed way 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 is the f irst 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 GETSandISATmethods can be implemented with the R package gets.