Time series cross-validation in ENET

EViews 12 has added several new enhancements to ENET (elastic net) such as the ability to add observation and variable weights and additional cross-validation methods.

In this blog post we will show one of the new methods for time series cross-validation. The demonstration will compare the forecasting performance of rolling window cross-validation with models constructed from least squares as well as a simple split of our dataset into training and test sets.

We will be evaluating the out-of-sample prediction abilities of this new technique on some important macroeconomic variables. The analysis will show the promising forecast performance obtained on the variables in this dataset by using a time series specific cross validation method compared with simpler methods.

New Variable Selection Diagnostics and Data Members

The 2021/03/03 update to EViews 12 has two new smaller Variable Selection features. These will help you extract information on the outcome of any selection method and obtain diagnostics on the selection process for a subset of methods. 

Lasso Variable Selection

In this blog post we will show how Lasso variable selection works in EViews by comparing it with a baseline least squares regression. We will be evaluating the prediction and variable selection properties of this technique on the same dataset used in the well-known paper “Least Angle Regression” by Efron, Hastie, Johnstone, and Tibshirani. The analysis will show the generally superior in-sample fit and out-of-sample forecast performance of Lasso variable selection compared with a baseline least squares model.

Univariate GARCH Models with Skewed Student’s-t Errors

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce a new add-in (i.e. SKEWEDUGARCH) that extends the current capability of EViews’ available features for the estimation of univariate GARCH models.

Automatic Factor Selection: Working with FRED-MD Data

This is the first of two posts devoted to automatic factor selection and panel unit root tests with cross-sectional dependence. Both features were recently released with EViews 12. Here, we summarize and work with two seminal contributions to automatic factor selection by Bai and Ng (2002) and Ahn and Horenstein (2013).

Using Indicator Saturation to Detect Outliers and Structural Shifts

One of the potential pitfalls when working with time series datasets is that the data may have temporary or permanent changes to its levels. These changes could be single time-period outliers, or a fundamental structural shift.

EViews 12 introduces a new technique to detect and model these outliers and structural changes through indicator saturation. in the recently released EViews 12, we thought we'd give another demonstration.

Nowcasting GDP with PMI using MIDAS-GETS

Nowcasting, the act of predicting the current or near-future state of a macro-economic variable, has become one of the more popular research topics performed in EViews over the past decade.

Perhaps the most important technique in nowcasting is mixed data sampling, or MIDAS. We have discussed MIDAS estimation in EViews in a couple of prior guest blog posts, but with the introduction of a new MIDAS technique in the recently released EViews 12, we thought we'd give another demonstration.