![]() The indicator saturation approach works by including indicator variables for outliers or structural breaks at every observation in the regression, and then employing the GETS algorithms to select which of the included variables should be retained in a final regression model. The indicator saturation approach is an extension of least squares regression for testing for outliers and structural breaks in a regression specification. The General-To-Specific auto-search/GETS algorithm follows the steps suggested by AutoSEARCH algorithm of Escribano and Sucarrat 2011, which in turn builds upon the work in Hoover and Perez 1999.ĮViews 12 adds regression tools for testing for the existence of outliers and structural breaks in a regression specification based on the indicator saturation approach. While LASSO estimation was available in previous versions of EViews, EViews 12 allows you to use the LASSO estimation technique purely as a variable selection method. LASSO variable selection has become the go-to method of variable selection in modern econometrics. The first three of these were introduced fifteen years ago with EViews 6, but the modern, more popular, techniques of LASSO and Auto-search/GETS are new in EViews 12. Variable selection, or feature selection as it is sometimes called in computer science literature, is an important component of modern machine learning. Elastic Net, Ridge and LASSO Enhancements.EViews 12 includes a number of new estimation techniques: ![]()
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