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:

Principal Component Analysis: Part I (Theory)

Most students of econometrics are taught to appreciate the value of data. We are generally taught that more data is better than less, and that throwing data away is almost "taboo". While this is generally good practice when it concerns the number of observations per variable, it is not always recommended when it concerns the number of variables under consideration. In fact, as the number of variables increases, it becomes increasingly more difficult to rank the importance (impact) of any given variable, and can lead to problems ranging from basic overfitting, to more serious issues such as multicollinearity or model invalidity. In this regard, selecting the smallest number of the most meaningful variables -- otherwise known as dimensionality reduction -- is not a trivial problem, and has become a staple of modern data analytics, and a motivation for many modern techniques. One such technique is Principal Component Analysis (PCA).

Dissecting the business cycle and the BBQ add-in

Authors and guest blog by Davaajargal Luvsannyam and Khuslen Batmunkh

Dating of business cycle is a very crucial for policy makers and businesses. Business cycle is the upward and downward trend of the production or business. Especially macro business cycle, which represents the general economic prospects, plays important role for policy and management decisions. For instance, when the economy is in downtrend companies tend to act more conservative. In contrast, when the economy is in uptrend companies tend to act more aggressive with the purpose of enhancing their market share. Keynesian business cycle theory suggests that business cycle is an important indicator for monetary policy which is able to stabilize the fluctuations of the economy. Therefore accurate dating of business cycle can be fundamental to efficient and practical policy decisions.

Using Facebook Likes and Google Trends data to forecast tourism

This post is guest authored by Ulrich Gunter, Irem Önder, Stefan Gindl, all from MODUL University Vienna, and edited by the EViews team.  (Note: all images on this post are for illustrative purposes only; are not taken from the published article and do not represent the exact analysis performed for the article). 

A recent article, "Exploring the predictive ability of LIKES of posts on the Facebook pages of four major city DMOs in Austria" in the scholarly journal Tourism Economics investigates the predictive ability of Facebook “likes” and Google Trends data on tourist arrivals in four major Austrian cities.  The use of online “big data” to perform short term forecasts or nowcasts is becoming increasingly important across all branches of economic study, but is particularly powerful in tourism economics.