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.

Wavelet Analysis: Part II (Applications in EViews)

This is the second of two entries devoted to wavelets. Part I was devoted to theoretical underpinnings. Here, we demonstrate the use and application of these principles to empirical exercises using the wavelet engine released with EViews 12.

Wavelet Analysis: Part I (Theoretical Background)

This is the first of two entries devoted to wavelets. Here, we summarize the most important theoretical principles underlying wavelet analysis. This entry should serve as a detailed background reference when using the new wavelet features released in EViews 12. In Part II we will apply these principles and demonstrate how they are used with the new EViews 12 wavelet engine.

Time Series Methods for Modelling the Spread of Epidemics

Authors and guest post by Eren Ocakverdi

This blog piece intends to introduce two new add-ins (i.e. SEIRMODEL and TSEPIGROWTH) to EViews users’ toolbox and help close the gap between epidemiological models and time series methods from a practitioner’s point of view.

Mapping COVID-19: Follow-up

As a follow up to our previous blog entry describing how to import Covid-19 data into EViews and produce some maps/graphs of the data, this post will produce a couple more graphs similar to ones we've seen become popular across social media in recent days.

Mapping COVID-19

With the world currently experiencing the Covid-19 crisis, many of our users are working remotely (aside: for details on how to use EViews at home, visit our Covid licensing page) anxious to follow data on how the virus is spreading across parts of the world. There are many sources of information on Covid-19, and we thought we’d demonstrate how to fetch some of these sources directly into EViews, and then display some graphics of the data. (Please visit our follow up post for a few more graph examples).

Beveridge-Nelson Filter

Authors and guest post by Benjamin Wong (Monash University) and Davaajargal Luvsannyam (The Bank of Mongolia)

Analysis of macroeconomic time series often involves decomposing a series into a trend and cycle components. In this blog post, we describe the Kamber, Morley, and Wong (2018) Beveridge-Nelson (BN) filter and the associated EViews add-in.