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The univariate MIDAS estimator in EViews 12 has been enhanced to use the Auto-Search/GETS algorithm to determine the which high-frequency variables to include in a U-MIDAS environment, as well as including Indicator Saturation methods.Ī practical demonstration of MIDAS-GETS used in nowcasting is available on the EViews blog. “White period” for clustering by cross-section, to indicate that there was between periodĮViews 12 extends these tools to allow for computation of robust covariances when clustersĪre defined by both cross-section units and periods ( Petersen 2009, Thompson 2011, Cameron, Gelbach, and Miller 2015). That there was contemporaneous correlation between cross-section units, and termed Following the lead of the system estimation literature, these robust standardĮrror calculations were termed “White cross-section” for clustering by period, to indicate In a panel equation and pool settings, versions of EViews prior to EViews 12 offered tools forĬomputing coefficient covariances accounting for clusters defined by cross-section units orīy periods. New model selection views for displaying cross-validation results.
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Time series based cross-validation methods.We have enhanced ENET in EViews 12 with the following features: The FIEGARCH model of Bollerslev and Mikkelsen (1996)Įlastic Net, Ridge and LASSO EnhancementsĮlastic Net (ENET) estimation, including the Ridge Regression and LASSO Estimation models, was added in EViews 11, and has proven a popular addition to the machine learning tools in EViews.The FIGARCH model of Baillie, Bollerslev and Mikkelsen (1996).GARCH models have been a fundamental part of the EViews estimation tool kit for over thirty years, however the traditional GARCH models estimated by EViews have focused on the short term dynamics of conditional variance.ĮViews 12 introduces two new GARCH model that capture long run dependence properties of variance. The included variables should be retained in a final regression model. Observation in the regression, and then employing the GETS algorithms to select which of The indicator saturationĪpproach works by including indicator variables for outliers or structural breaks at every Outliers and structural breaks in a regression specification. The indicator saturation approach is an extension of least squares regression for testing for Specification based on the indicator saturation approach. Indicator SaturationĮViews 12 adds regression tools for testing for the existence of outliers and structural breaks in a regression The General-To-Specific auto-search/GETS algorithm follows the steps suggested by AutoSEARCH algorithm ofĮscribano and Sucarrat 2011, which in turn builds upon the work in Hoover and Perez 1999. 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.
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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, isĪn important component of modern machine learning.