Estimation of Local Linear Trend via Kalman Filter

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce a new add-in (i.e. LOCALLINEAR) that estimates local linear trends via Kalman filter.

Seasonal Adjustment in EViews 14 – Calendar Variables and Lunar New Year

In our previous blog post we examined how JDemetra+ can be used to perform seasonal adjustment in EViews, producing results identical to (or at least extremely similar to) those given by the ubiquitous X-13 package from the US Census.

The previous post didn’t discuss one of the more popular features of seasonal adjustment; calendar adjustments. In this post we’ll rectify that by demonstrating how to use calendar adjustments in JDemetra+, as well as how to create your own calendar effects, should you wish to build upon the built-in features. In particular we’ll demonstrate adding a Lunar (Chinese) New Year variable for JDemetra+.

Seasonal Adjustment in EViews 14 – Comparing X-13 and JDemetra+

Seasonal Adjustment is a mainstay of modern macro-economic analysis. Many economic time series exhibit seasonal fluctuations that need to be removed from the underlying patterns of the data before econometric analysis can be performed. In this two-part series we'll use EViews 14 to compare JDemetra+ to X13 in Part 1 and look at how to use JDemetra+ with the Lunar New Year in Part 2.

Generalized Autoregressive Score (GAS) approach to univariate GARCH Models

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce a new add-in (i.e. GASMODELU) that estimates selected univariate GARCH models within the Generalized Autoregressive Score (GAS) framework.

From Bańbura et al. (2010) to Cascaldi-Garcia’s (2022) Pandemic Priors

Authors and guest post by Ole Rummel and Davaajargal Luvsannyam This is the second in a series of blog posts that will present EViews add-in, LBVAR, aimed at estimating and forecasting a large Bayesian VAR model due to Banbura, Giannone and Reichlin (2010). We will discuss and replicate Cascaldi-Garcia (2022) on this blog.

Principal Component Analysis for Nonstationary Series

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce a new add-in (i.e. HXPRINCOMP) that implements the procedure developed by Hamilton and Xi (2022).

Nowcasting US GDP During Covid-19 using Factor Augmented MIDAS

The COVID-19 pandemic sent waves through the global economy, triggering a macroeconomic shock and caused unprecedented challenges for economists trying to predict the current state of economies. In the quest for a more timely and accurate assessment of economic conditions during the COVID-19 era, economists and researchers turned to innovative solutions, and one of the most promising techniques emerged: MIDAS (Mixed-Data Sampling) estimation.