SpecEval Add-In - Part 2

Authors and guest post by Kamil Kovar

This is the second in a series of blog posts (the first can be found here) that present a new EViews add-in, SpecEval, aimed at facilitating development of time series models used for forecasting. This blog post will focus on the illustration of the basic outputs of the add-in by following a simple application, which will also illustrate the model development process that the add-in aims to facilitate. Next section provides brief discussion of this process, while the following section discusses the data and models considered. The main content of this blog post is contained in next two sections, which discuss basic execution before presenting the actual application.

Box-Cox Transformation and the Estimation of Lambda Parameter

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce a new add-in (i.e. BOXCOX) that can be used in applying power transformations to the series of interest and provides alternative methods to estimate the optimal lambda parameter to be used in transformation.

SpecEval Add-In

Authors and guest post by Kamil Kovar

This is the first in a series of blog posts that will present a new EViews add-in, SpecEval, aimed at facilitating time series model development. This blog post will focus on the motivation and overview of the add-in functionality. Remaining blog posts in this series will illustrate the use of the add-in.

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).