Monday, October 14, 2019

Sign Restricted VAR Add-In

Authors and guest post by Davaajargal Luvsannyam and Ulziikhutag Munkhtsetseg

Nowadays, sign restricted VARs (SRVARs) are becoming popular and can be considered as an indispensable tool for macroeconomic analysis. They have been used for macroeconomic policy analysis when investigating the sources of business cycle fluctuations and providing a benchmark against which modern dynamic macroeconomic theories are evaluated. Traditional structural VARs are identified with the exclusion restriction which is sometimes difficult to justify by economic theory. In contrast, SRVARs can easily identify structural shocks since in many cases, economic theory only offers guidance on the sign of structural impulse responses on impact.

Wednesday, July 17, 2019

Pyeviews update: now compatible with Python 3

If you’re a user of both EViews and Python, then you may already be aware of pyeviews (if not, take a look at our original blog post here or our whitepaper here). 

Pyeviews has been updated and is now compatible with Python 3. We’ve also added support for numpy structured arrays and several additional time series frequencies. 

You can get these updates through pip:

pip install pyeviews

Through the conda-forge channel in Anaconda:

conda install pyeviews -c conda-forge

Or by typing:

python setup.py install

in your installation directory.



Wednesday, June 26, 2019

Bayesian VAR Prior Comparison

EViews 11 introduces a completely new Bayesian VAR engine that replaces one from previous versions of EViews. The new engine offers two new major priors; the Independent Normal-Wishart and the Giannone, Lenza and Primiceri, that compliment the previously implemented Minnesota/Litterman, Normal-Flat, Normal-Wishart and Sims-Zha priors. The new priors were enhanced with new options for forming the underlying covariance matrices that make up essential components of the prior.

Monday, May 13, 2019

Functional Coefficient Estimation: Part I (Nonparametric Estimation)

Recently, EViews 11 introduced several new nonparametric techniques. One of those features is the ability to estimate functional coefficient models. To help familiarize users with this important technique, we're launching a multi-part blog series on nonparametric estimation, with a particular focus on the theoretical and practical aspects of functional coefficient estimation. Before delving into the subject matter however, in this Part I of the series, we give a brief and gentle introduction to some of the most important principles underlying nonparametric estimation, and illustrate them using EViews programs.

Tuesday, April 23, 2019

Generalized Autoregressive Score (GAS) Models: EViews Plays with Python

Starting with EViews 11, users can take advantage of communication between EViews and Python. This means that workflow can begin in EViews, switch over to Python, and be brought back into EViews seamlessly. To demonstrate this feature, we will use U.S. macroeconomic data on the unemployment rate to fit a GARCH model in EViews, transfer the data over and estimate a GAS model equivalent of the GARCH model in Python, transfer the data back to EViews, and compare the results.

Seasonal Unit Root Tests

Author and guest post by Nicolas Ronderos

In this blog entry we will offer a brief discussion on some aspects of seasonal non-stationarity and discuss two popular seasonal unit root tests. In particular, we will cover the Hylleberg, Engle, Granger, and Yoo (1990) and Canova and Hansen (1995) tests and demonstrate practically using EViews how the latter can be used to detect the presence of seasonal unit roots in a US macroeconomic time series. All files used in this exercise can be downloaded at the end of the entry.

Friday, February 1, 2019

Time varying parameter estimation with Flexible Least Squares and the tvpuni add-in

Author and guest post by Eren Ocakverdi

Professional life of a researcher who follows or responsible from an emerging market can become so miserable when things suddenly change and the past experience does not hold anymore. As a practitioner you can get used to it over time, but it’s a whole different story when it comes to identifying empirical relationships between market indicators as part of your job.

History can be a really good gauge to understand how such indicators are linked to one another only if you look through a proper glass. Abrupt changes, structural breaks or transition periods may alter such relationships so much that they would be misidentified with those traditional methods where the underlying structure is assumed fixed over the full sample.