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.

State Space Models with Fat-Tailed Errors and the sspacetdist add-in

Author and guest post by Eren Ocakverdi.


Linear State Space Models (LSSM) provide a very useful framework for the analysis of a wide range of time series problems. For instance; linear regression, trend-cycle decomposition, smoothing, ARIMA, can all be handled practically and dynamically within this flexible system.
One of the assumptions behind LSSM is that the errors of the measurement/signal equation are normally distributed. In practice, however, there are situations where this may not be the case and errors follow a fat-tailed distribution. Ignoring this fact may result in wider confidence intervals for the estimated parameters or may cause outliers to bias parameter estimates.

10+ New Features Added to EViews 10

EViews 10+ is a free update to EViews 10, and introduces a number of new features, including:

• Chow-Lin, Denton and Litterman frequency conversion with multiple indicator series.
• Model dependency graphs.
• US Bureau of Labor Statistics (BLS) data connectivity.
• Introduction of the X-13 Force option for forcing annual totals.
• Expansion of the EViews 10 snapshot system to program files.
• A new help command.

All current EViews 10 users can receive the following new features. To update your copy of EViews 10, simply use the built in update feature (Help->EViews Update), or manually download the latest EViews 10 patch.

Dumitrescu-Hurlin Panel Granger Causality Tests: A Monte Carlo Study

With data availability at its historical peak, time series panel econometrics is in the limelight. Unlike traditional panel data in which each cross section $i = 1, \ldots, N$ is associated with $t=1, \ldots, T < N$ observations, what characterizes time series panel data is that $N$ and $T$ can both be very large. Moreover, the time dimension also gives rise to temporal dynamic information and with it, the ability to test for serial correlation, unit roots, cointegration, and in this regard, also Granger causality.

Hamilton’s “Why you should never use the Hodrick-Prescott Filter”

Professor James D. Hamilton requires no introduction, having been one of the most important researchers in time series econometrics for decades.

Over the past few years, Hamilton has been working on a paper calling on applied economists to abandon the ubiquitous Hodrick-Prescott Filter and replace it with a much simpler method of extracting trend and cycle information from a time series.

This paper has become popular, and a number of our users have asked how to replicate it in EViews. One of our users, Greg Thornton, has written an EViews add-in (called Hamilton) that performs Hamilton’s method.  However, given its relative simplicity, we thought we’d use a blog post to show manual calculation of the method and replicate the results in Hamilton’s paper.

AutoRegressive Distributed Lag (ARDL) Estimation. Part 3 - Practice

In Part 1 and Part 2 of this series, we discussed the theory behind ARDL and the Bounds Test for cointegration. Here, we demonstrate just how easily everything can be done in EViews 9 or higher.

While our two previous posts in this series have been heavily theoretically motivated, here we present a step by step procedure on how to implement Part 1 and Part 2 in practice.

AutoRegressive Distributed Lag (ARDL) Estimation. Part 2 - Inference

This is the second part of our AutoRegressive Distributed Lag (ARDL) post. For Part 1, please go here, and for Part 3, please visit here.

In this post we outline the correct theoretical underpinning of the inference behind the Bounds test for cointegration in an ARDL model. Whilst the discussion is by its nature quite technical, it is important that practitioners of the Bounds test have a grasp of the background behind its inferences.