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.

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.

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.

Panel Structural VARs and the PSVAR add-in

Author and guest blog by Davaajargal Luvsannyam

Panel SVARs have been used to address a variety of issues of interest to policymakers and applied economists. Panel SVARs are particularly suitable to analyze the transmission of idiosyncratic shocks across units and time. For example, Canova et al. (2012) have studied how U.S. interest rate shocks are propagated to 10 European economies, 7 in the Euro area and 3 outside of it, and how German shocks are transmitted to the remaining nine economies. 

Nowcasting GDP on a Daily Basis

Author and guest blog by Michael Anthonisz, Queensland Treasury Corporation.
In this blog post, Michael demonstrates the use of MIDAS in EViews to nowcast Australian GDP growth on a daily basis.

"Nowcasts" are forecasts of the here and now ("now" + "forecast" = "nowcast"). They are forecasts of the present, the near future or the recent past. Specifically, nowcasts allow for real-time tracking or forecasting of a lower frequency variable based on other series which are released at a similar or higher frequency.

Principal Component Analysis: Part II (Practice)

In Part I of our series on Principal Component Analysis (PCA), we covered a theoretical overview of fundamental concepts and disucssed several inferential procedures. Here, we aim to complement our theoretical exposition with a step-by-step practical implementation using EViews. In particular, we are motivated by a desire to apply PCA to some dataset in order to identify its most important features and draw any inferential conclusions that may exist. We will proceed in the following steps: